Lead Data Engineer

🏢 Summary: Lead Distributed Data Platform Engineer responsible for architecting and delivering enterprise-scale lakehouse and distributed data platforms to enable advanced analytics and reporting. The role combines hands-on technical leadership with team mentorship, driving scalable, secure, and high-performance data solutions in cloud-native environments. You will guide architectural decisions, enforce engineering best practices, and ensure platform reliability at scale. 🗂️ Requirements: Proven experience leading data engineering or platform teams, Strong programming skills in Python, Strong programming skills in SQL, Hands-on experience with Apache Spark in production, Experience with Delta Lake and/or Apache Iceberg in production, Experience designing distributed systems and lakehouse architectures, Experience building scalable data pipelines, Knowledge of CI/CD and automated testing practices, Experience with Kubernetes and Docker, Experience working in cloud-native environments 📃 Skills: Python, SQL, Spark, Delta, Iceberg, dbt, Databricks, Snowflake, Kubernetes, Docker, CI/CD, Java, Scala, Rust 🏢 Description: ABOUT THE COMPANY We are a global legal technology company that has been building software for the legal industry for over two decades. Our AI-powered cloud platform is used by leading law firms, Fortune 500 corporations, and government agencies worldwide to organise complex data, surface critical insights, and act on them — across litigation, investigations, regulatory inquiries, and data breach response. We're valued at $3.6 billion and invest over $170 million annually in R&D. We're making substantial investments in data lake technology and distributed systems to support future growth and advanced analytics. Our scale means the data problems here are genuinely hard — and the platform you lead will underpin how the entire organisation accesses and acts on its data. ABOUT THE ROLE We're building a specialised team focused on enabling advanced analytics and reporting capabilities across our internal data ecosystem. As Lead Distributed Data Platform Engineer, you'll combine deep technical expertise with hands-on team leadership — guiding a team in designing and maintaining data platforms that integrate modern lakehouse technologies, distributed compute frameworks, and cloud-native services at enterprise scale. You'll lead architectural decisions, mentor engineers, and ensure delivery of secure, reliable, and scalable solutions. The role emphasises technical leadership, governance best practices, and a culture of innovation and continuous improvement. You'll also participate in on-call rotations as part of shared team responsibility for platform reliability. WHAT YOU'LL WORK ON Team leadership and mentorship Lead and mentor a team of data platform engineers, promoting collaboration, knowledge sharing, and professional growth. Set and maintain high engineering standards across the team. Distributed systems architecture Drive architectural decisions for distributed systems and lakehouse platforms using Spark, Delta Lake, and Iceberg. Facilitate architecture reviews and contribute to design decisions for fault-tolerant, future-ready systems. Data pipeline and platform delivery Oversee design and implementation of scalable data pipelines and analytics workflows, ensuring they are reliable, performant, and maintainable at scale. Engineering best practices Ensure adherence to clean code, modular design, CI/CD, automated testing, and code review standards across all platform engineering work. Performance and cost optimisation Manage performance tuning, scalability strategies, and cost optimisation across cloud-native environments and large-scale distributed workloads. Governance and observability Champion governance, observability, and compliance frameworks across all data platforms — ensuring data remains accessible, secure, and auditable. Stakeholder communication Communicate effectively with leadership and cross-functional teams to provide updates, resolve blockers, and ensure delivery aligns with business objectives and analytics needs. WHAT WE LOOK FOR Proven technical team leadership Demonstrated experience leading data engineering or platform development teams — mentoring engineers, owning architectural decisions, and driving delivery outcomes. Python and SQL Strong programming skills in both Python and SQL applied to production data platform work at scale. Apache Spark Hands-on experience with Spark for distributed data processing, including performance tuning and optimisation in production environments. Lakehouse architecture Expertise in Delta Lake and/or Apache Iceberg. You understand the trade-offs and have applied these technologies in production at scale. Analytics tooling Familiarity with dbt, Databricks, and Snowflake for analytics workflows and large-scale data processing. Software engineering fundamentals Solid understanding of software engineering principles — CI/CD, automated testing, clean code, and modular design applied to data platform systems. Infrastructure and containerisation Familiarity with Kubernetes, Docker, and infrastructure-as-code tools in cloud-native environments. Communication and stakeholder management Strong communication skills with the confidence to operate across engineering teams, cross-functional partners, and senior leadership. Bonus Exposure to event-driven architectures and advanced analytics platforms. Experience enabling self-service analytics for internal stakeholders. Experience in Java, Scala, or Rust. Exposure to service mesh and advanced orchestration patterns. THE TEAM You'll join a global engineering organisation working on a platform used by some of the world's largest legal teams. The culture is diverse, inclusive, and driven by high standards. Engineers here work on genuinely complex technical problems at scale — and are supported with the coaching, development, and tooling to keep growing. COMPENSATION & BENEFITS Salary 270,000 – 406,000 PLN per year, plus an annual performance bonus and long-term incentives. Health coverage Comprehensive health, dental, and vision plans. Parental leave Parental leave available for both primary and secondary caregivers. Flexible working Flexible work arrangements with a remote-first model. Company breaks Two week-long company-wide breaks per year, plus additional time off. Training investment Dedicated training investment programme to support ongoing professional development.

🏢 Summary: Senior Data Platform Engineer role focused on building and optimising a cloud-native lakehouse platform for large-scale analytics and reporting. The position involves designing distributed data pipelines, enabling self-service analytics, and implementing governance and observability frameworks using modern data technologies. You will work with Spark-based systems and integrated data warehousing solutions to deliver scalable, reliable data platforms. 🗂️ Requirements: Strong programming skills in Python, Strong programming skills in SQL, Hands-on experience with Apache Spark in production environments, Experience with Delta Lake and/or Apache Iceberg in production, Practical experience with dbt for data transformations, Experience with Databricks and Snowflake, Understanding of data governance and lineage in large-scale environments, Familiarity with Kubernetes and Docker, Experience with CI/CD and automated testing practices, Ability to participate in on-call rotations 📃 Skills: Python, SQL, Spark, Delta, Iceberg, dbt, Databricks, Snowflake, Kubernetes, Docker, CI/CD 🏢 Description: ABOUT THE COMPANY We are a global legal technology company that has been building software for the legal industry for over two decades. Our AI-powered cloud platform is used by leading law firms, Fortune 500 corporations, and government agencies worldwide to organise complex data, surface critical insights, and act on them — across litigation, investigations, regulatory inquiries, and data breach response. We're valued at $3.6 billion and invest over $170 million annually in R&D. We're making substantial investments in data lake technology and distributed systems to support future growth and advanced analytics. Our scale means the data problems here are genuinely hard — and the platforms you build will have real consequence across the organisation. ABOUT THE ROLE We're building a specialised team focused on enabling advanced analytics and reporting capabilities across our internal data ecosystem. As a Senior Data Platform Engineer, you'll combine strong software engineering principles with deep data expertise to build robust, cloud-native platforms that process large-scale datasets efficiently and enable internal teams to build reporting and analytics on top of them. The role emphasises cloud-native architecture, lakehouse integration, data warehousing, and governance best practices. You'll work on systems using Apache Spark, Delta Lake, and Iceberg, and help deliver curated data models and self-service analytics capabilities to internal stakeholders. You'll also participate in on-call rotations as part of shared team responsibility. WHAT YOU'LL WORK ON Data pipeline and distributed systems Design and implement scalable data pipelines and distributed systems using Spark and Python to process and transform large-scale datasets for analytics and reporting. Lakehouse platform development Develop and maintain lakehouse capabilities with Delta Lake and Iceberg, ensuring data reliability, versioning, and performance optimisation at scale. Analytics workflow enablement Integrate dbt for SQL transformations running on Spark. Collaborate with internal teams to deliver curated datasets and self-service analytics capabilities for reporting and advanced use cases. Data warehousing optimisation Integrate and optimise Databricks and Snowflake for scalable storage and query performance. Drive performance tuning and cost optimisation across Spark jobs and cloud-native environments. Governance and observability Implement observability and governance frameworks including data lineage, quality checks, and compliance controls. Build platforms that allow secure and compliant access to diverse data sources. Engineering best practices Apply and champion clean code, modular design, CI/CD, automated testing, and code review standards across all data engineering work. On-call participation Participate in on-call rotations as part of shared team responsibility for platform reliability. WHAT WE LOOK FOR Python and SQL Strong programming skills in both Python and SQL, applied to production data platform work at scale. Apache Spark Solid hands-on experience with Spark for distributed data processing, including performance tuning in production environments. Lakehouse architecture Expertise in Delta Lake and/or Apache Iceberg. You've applied these in production and understand the trade-offs in real-world scenarios. dbt and analytics tooling Practical experience with dbt for transformation workflows. Familiarity with Databricks and Snowflake for large-scale analytics workloads. Data governance and compliance Understanding of data governance, lineage tracking, and compliance requirements in large-scale, multi-tenant data environments. Infrastructure and containerisation Familiarity with Kubernetes, Docker, and infrastructure-as-code tools in cloud-native environments. Software engineering fundamentals Solid understanding of software engineering principles — CI/CD, automated testing, clean code, and modular design applied to data systems. Bonus Exposure to event-driven architectures and advanced analytics platforms. Experience enabling self-service analytics for internal stakeholders. Experience in Java, Scala, or Rust. THE TEAM You'll join a global engineering organisation working on a platform used by some of the world's largest legal teams. The culture is diverse, inclusive, and driven by high standards. Engineers here work on genuinely complex technical problems at scale — and are supported with the coaching, development, and tooling to keep growing. COMPENSATION & BENEFITS Salary 208,000 – 312,000 PLN per year, plus an annual performance bonus and long-term incentives. Health coverage Comprehensive health, dental, and vision plans. Parental leave Parental leave available for both primary and secondary caregivers. Flexible working Flexible work arrangements with a remote-first model. Company breaks Two week-long company-wide breaks per year, plus additional time off. Training investment Dedicated training investment programme to support ongoing professional development.

🏢 Summary: The role focuses on designing and building scalable, cloud-native data platforms to enable advanced analytics and reporting across a large internal data ecosystem. It involves developing distributed data pipelines, lakehouse architectures, and optimised data warehousing solutions with strong emphasis on performance, governance, and reliability. The position requires deep technical expertise in big data technologies and cloud-native infrastructure, including on-call responsibility for platform stability. 🗂️ Requirements: Strong programming skills in Python, Strong programming skills in SQL, Commercial experience with Apache Spark for distributed data processing, Hands-on experience with Delta Lake and/or Apache Iceberg in production, Experience with dbt for SQL transformations, Experience with Databricks and Snowflake, Knowledge of CI/CD and automated testing practices, Experience with Kubernetes and Docker, Understanding of performance tuning and cost optimisation in large-scale data systems, Ability to participate in on-call rotations 📃 Skills: Python, SQL, Spark, Delta, Iceberg, dbt, Databricks, Snowflake, Kubernetes, Docker, CI/CD 🏢 Description: ABOUT THE COMPANY We are a global legal technology company that has been building software for the legal industry for over two decades. Our AI-powered cloud platform is used by leading law firms, Fortune 500 corporations, and government agencies worldwide to organise complex data, surface critical insights, and act on them — across litigation, investigations, regulatory inquiries, and data breach response. We're valued at $3.6 billion and invest over $170 million annually in R&D. Over 75% of our business has transitioned to our cloud platform, and we are making substantial investments in data lake technology and distributed systems to support future growth and advanced analytics. Our scale means the data problems here are genuinely hard — and the infrastructure you build will have real consequence. ABOUT THE ROLE We're building a specialised team focused on enabling advanced analytics and reporting capabilities across our internal data ecosystem. As an Advanced Data Platform Engineer, you'll design and implement scalable, cloud-native data platforms that integrate modern lakehouse technologies, distributed compute frameworks, and cloud-native services to support diverse analytical use cases at enterprise scale. The role emphasises technical depth — performance optimisation, governance best practices, and the kind of engineering rigour that keeps vast datasets accessible, secure, and compliant. You'll work closely with internal teams to deliver curated datasets and self-service analytics capabilities, and you'll participate in on-call rotations as part of shared team responsibility. WHAT YOU'LL WORK ON Data pipeline and distributed systems design Design and implement complex data pipelines and distributed systems using Spark and Python, applying clean code principles, modular design, CI/CD, automated testing, and thorough code reviews. Lakehouse platform development Develop and maintain lakehouse capabilities with Delta Lake and Apache Iceberg, ensuring reliability, performance, and long-term maintainability at scale. Analytics workflow enablement Integrate dbt for SQL transformations running on Spark. Deliver curated datasets and self-service analytics capabilities that empower internal stakeholders to explore data independently. Data warehousing optimisation Optimise Databricks and Snowflake environments for performance and scalability. Drive cost optimisation and performance tuning across Spark jobs and cloud-native infrastructure. Observability and governance Implement observability and governance frameworks including data lineage tracking and compliance controls, ensuring data remains secure and auditable. On-call participation Participate in on-call rotations as part of shared team responsibility for platform reliability. WHAT WE LOOK FOR Python and SQL Strong programming skills in Python and SQL — the foundation for everything you'll build here. Apache Spark Solid experience with Spark for distributed data processing at scale, including performance tuning and optimisation. Lakehouse architecture Expertise in Delta Lake and/or Apache Iceberg. You understand the tradeoffs and have used these in production environments. Analytics tooling Familiarity with dbt, Databricks, and Snowflake for analytics workflows and SQL transformation pipelines. Software engineering fundamentals Solid understanding of software engineering principles — CI/CD, automated testing, clean code, and modular design applied to data systems. Infrastructure and containerisation Familiarity with Kubernetes, Docker, and infrastructure-as-code tools in cloud-native environments. Scalability and cost optimisation Understanding of performance tuning, scalability strategies, and cost optimisation for large-scale data systems. Bonus Exposure to event-driven architectures and advanced analytics platforms. Experience enabling self-service analytics for internal stakeholders. Experience in Java, Scala, or Rust. THE TEAM You'll join a global engineering organisation working on a platform used by some of the world's largest legal teams. The culture is diverse, inclusive, and driven by high standards. Engineers here work on genuinely complex technical problems at scale — and are supported with the coaching, development, and tooling to keep growing. COMPENSATION & BENEFITS Salary 160,000 – 240,000 PLN per year, plus an annual performance bonus and long-term incentives. Health coverage Comprehensive health, dental, and vision plans. Parental leave Parental leave available for both primary and secondary caregivers. Flexible working Flexible work arrangements with a remote-first model. Company breaks Two week-long company-wide breaks per year, plus additional time off. Training investment Dedicated training investment programme to support ongoing professional development.

🏢 Summary: Lead Data Engineer role focused on driving AI initiatives and building scalable cloud-based data architectures in a global environment. The position involves technical ownership of data platforms, designing secure and high-performing systems, and leading data engineering efforts in a DevOps setting. The role emphasizes AI integration, cloud infrastructure, and enterprise-grade data governance. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python in production, Strong experience designing and scaling complex data systems, Hands-on experience with AI technologies, Hands-on experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs and data integration, Experience with automation tools, Knowledge of data governance practices, Understanding of data security and compliance standards, Proven experience leading and mentoring engineers 📃 Skills: Python, SQL, AWS, Azure, Java, AI, RAG, MCP, APIs, DevOps, Automation, Monitoring, Cloud, DataEngineering, DataPipelines, Governance, Security, Compliance, Backend 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: Lead Data Engineer role focused on building scalable AI and cloud-based data solutions in a global environment. The position involves leading data engineering initiatives, designing secure cloud architectures, and supporting AI applications using AWS or Azure. The offer includes hybrid work, professional development opportunities, and a comprehensive benefits package. 🗂️ Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience, 8+ years in software engineering, 5+ years of backend development with Python, Experience designing and scaling complex data systems, Hands-on experience with AI technologies, Experience with AWS or Azure, Strong knowledge of Python and SQL, Experience with APIs, data integration, automation tools, and data governance, Understanding of data security and compliance standards, Leadership and mentoring experience, English and Polish proficiency (minimum B2) 📃 Skills: Python, SQL, AWS, Azure, Java, APIs, DevOps, AI, RAG, MCP, Automation, DataGovernance 🏢 Description: We are looking for an experienced Lead Data Engineer to drive AI and cloud-based data solutions within a global technology organization. In this role, you will lead data initiatives, shape scalable architectures, and collaborate with both technical teams and senior stakeholders to deliver secure and high-performing systems. Key Responsibilities: Act as the main point of contact for data access and system-related topics with senior stakeholders Lead and mentor data engineers, promoting best practices and technical excellence Design, build, and maintain scalable cloud infrastructure and data pipelines Ensure data quality, security, compliance, and governance across the full lifecycle Develop secure and reliable cloud architectures (AWS/Azure) for AI and enterprise applications Implement monitoring, alerting, disaster recovery, and business continuity solutions Take technical ownership of applications within a DevOps environment Drive automation and self-service capabilities Support AI initiatives (e.g., AI Agents, RAG, MCP) with focus on quality and scalability Stay updated on emerging technologies and advise on strategic data direction Requirements: Degree in Computer Science, AI, Data Science, Software Engineering, or equivalent experience 8+ years in software engineering, including 5+ years of backend development with Python (production level) Strong experience designing and scaling complex data systems Hands-on experience with AI technologies and cloud platforms (AWS or Azure) Solid knowledge of Python, SQL (Java is a plus) Experience with APIs, data integration, automation tools, and data governance Strong understanding of data security and compliance standards Proven leadership and mentoring experience Excellent communication skills in English and Polish (min. B2); German is a plus What We Offer: Opportunity to work in a global, international environment Real impact on AI and cloud solutions in a large-scale organization Access to training platforms and professional development programs Hybrid work model with flexible hours (modern office in central Wroclaw) Comprehensive benefits package (medical & dental care, sports card, life insurance, mental health program) Cafeteria benefits platform with monthly points CSR initiatives, integration events, and employee passion clubs

🏢 Summary: The offer is for a Software Engineer (Data) role focused on building scalable data infrastructure and production-ready ML systems within an AI-driven environment. The position combines backend engineering, data engineering, and MLOps to design high-throughput pipelines and support AI solutions from experimentation to production. The role involves close collaboration with Data Science teams to shape architecture and engineering standards for next-generation data platforms. 🗂️ Requirements: 4+ years of software engineering experience, Strong proficiency in Python, Experience building scalable production systems, Experience with data-intensive applications and SQL databases, Knowledge of data modeling and query optimization, Experience with Terraform, Kubernetes, Docker or similar tools, Understanding of CI/CD pipelines and Infrastructure as Code, Experience implementing automated testing and clean architecture principles, Ability to productionize ML solutions with Data Science teams 📃 Skills: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker, Airflow, SQL, CI/CD, AWS, Snowflake, DBT, MLOps 🏢 Description: Our client helps small teams power big businesses with the must-have platform for intelligent marketing automation. Customers from over 170 countries depend on the Client’s mix of pre-built automation and integration to power personalized marketing, transactional emails, and one-to-one CRM interactions throughout the customer lifecycle. We’re looking for a Software Engineer (Data) to join our client’s growing AI and Data organization and help build the scalable foundations behind next-generation data and ML systems. In this role, you won’t just be working with data infrastructure; you’ll be shaping the software architecture, engineering standards, and production-ready platforms that power the company’s AI ecosystem. This is more than a traditional backend or data engineering position. You’ll work at the intersection of software engineering, data, and AI, partnering closely with Data Science and AI teams to bridge the gap between experimentation and production. From designing resilient systems to building scalable MLOps pipelines, your work will directly influence how AI solutions are developed, deployed, and scaled across the organization. Key takeaways: Stack: Python, MySQL, PostgreSQL, Spark, Terraform, Kubernetes, Docker Salary : 20 000 - 23 000 PLN gross/month, Contract of employment (+10% annual bonus, 75% Creative Tax) Working model: Hybrid, once a week in the office Location: Krakow, ul. Konopnickiej Recruitment process: Call with MOTIFE recruiter (30 min) Interview with Hiring Manager (45 min) Technical interview, live coding (1h) Cross-functional interview (1h) Responsibilities: Design, develop, and maintain scalable high-throughput data pipelines across complex data ecosystems. Build and optimize data models, schemas, and database structures to ensure long-term scalability and performance. Implement engineering best practices, including automated testing, CI/CD pipelines, and Infrastructure as Code (Terraform). Partner closely with AI and Data Science teams to productionize machine learning solutions and develop scalable MLOps pipelines. Engineer and maintain feature stores and data infrastructure supporting AI-driven initiatives. Monitor, maintain, and improve the reliability and efficiency of containerized environments using Kubernetes, Docker, and Airflow. Ensure platform stability, observability, and operational excellence through proactive system monitoring and health checks. Collaborate cross-functionally with engineering and business stakeholders to translate complex technical concepts into actionable insights. Contribute to the architectural direction and scalability of the organization’s AI and data platforms. Drive the adoption of robust software engineering standards across data and infrastructure projects. Requirements: 4+ years of experience in software engineering, with strong hands-on experience in backend development and building scalable production systems. Strong proficiency in Python and solid software engineering fundamentals, including clean architecture, testing, and maintainable code practices. Experience working with data-intensive applications, databases, and SQL, including data modeling and query optimization. Exposure to modern data engineering, cloud, or infrastructure environments, with familiarity in tools such as Terraform, Kubernetes, Docker, or similar technologies. Understanding of CI/CD pipelines, Infrastructure as Code, and general engineering best practices. Interest in AI/ML ecosystems and willingness to work closely with Data Science and AI teams on productionizing ML solutions. Familiarity with cloud platforms and modern data stack technologies such as AWS, Snowflake, Spark, or DBT is considered a strong plus. Ownership mindset and comfort working in evolving, fast-moving environments where systems and processes are still being built. What we offer: Health Benefits 1. Medical Full coverage for employees and their dependents through LUX MED. Employees have access to the “Premium” package, providing enhanced coverage and greater access to care. A client pays 100% of the premium for employees and 50% for dependents. 2. Dental No additional cost for dental coverage- integrated into LUX MED medical plan. 3. Vision Reimbursement for vision expenses up to 400 PLN every 2 years. Mental Health Tools Access to TELUS Health EAP to provide support and resources in a time of need. Additional Benefits 10% annual bonus 75% Creative Tax Vacation: 26 days. Home Office Stipend: One-time $150 equivalent home office stipend to outfit their home office. Calm Subscription: Premium subscription access to Calm, the #1 app for sleep, meditation, and relaxation. Hub Perks: Receive meal and transportation benefits when traveling to the Poland Hub. Baby Swag: If you have a baby or adopt, you’ll receive a company-branded first bath bundle. Sabbatical Program: After 5 years of employment, receive a month-long paid sabbatical leave, with a sabbatical leave bonus.

🏢 Summary: Full Stack Technical Lead role focused on leading an engineering team delivering cloud-native digital insurance products. The position combines hands-on backend and frontend development with ownership of application architecture, API design, and technical standards. You will drive technical direction, ensure delivery quality, and collaborate closely with product, platform engineering, and business stakeholders. 🗂️ Requirements: 7+ years professional software engineering experience, Minimum 2 years in a technical leadership role, Strong backend development experience in Java and Spring Boot or equivalent, Frontend development experience in React or equivalent, Experience designing microservices architectures and REST APIs at production scale, Understanding of distributed system patterns, Experience designing services for Kubernetes environments, Familiarity with CI/CD and GitOps deployment models, Experience leading Agile sprint delivery and technical scoping, Experience in regulated industries such as insurance, finance, or healthcare 📃 Skills: Java, Spring, SpringBoot, React, Microservices, REST, Kubernetes, CI/CD, GitOps, Kafka, Azure, API, Docker 🏢 Description: About Webellian Webellian is a well-established Digital Transformation and IT consulting company committed to creating a positive impact for our clients. We strive to make a meaningful difference in diverse sectors such as insurance, banking, healthcare, retail, and manufacturing. Our passion for cutting-edge and disruptive technologies, as well as our shared values and strong principles, are what motivate us. We are a community of engineers and senior advisors who work with our clients across industries, playing a deep and meaningful role in accelerating and realizing their vision and strategy. About the position As a Full Stack Technical Lead you will lead an engineering team building digital insurance products and services. You are the technical authority for your team: you own the application architecture, set the engineering standards, and are responsible for the quality of everything the team ships. You will work within a cloud-native engineering environment where CI/CD pipelines, containerisation standards, and deployment tooling are provided centrally. Your role is to lead the application layer (service design, API architecture, and team delivery), not to own infrastructure. You are the primary technical interface between your product team and the central platform engineering function, and between engineering and the wider product and business stakeholders. This is a hands-on technical leadership role. You will be expected to remain close to the code while carrying the responsibility for the team's technical direction and delivery quality. Key responsibilities: Architecture and Technical Direction Own the application architecture for your product: service decomposition, API design, data models, event schemas, and integration patterns with core insurance systems. Make architectural decisions that are durable, well-reasoned, and clearly communicated to the team and stakeholders. Identify and manage technical debt: prioritise pragmatically, document trade-offs, and schedule remediation in delivery planning. Define technical standards for the team (coding conventions, testing practices, API versioning, observability instrumentation) and hold the team to them. Work within the deployment and containerisation standards provided by the central engineering platform; raise and influence changes to those standards through the appropriate channels. Delivery Leadership Translate business and product requirements into technical designs; lead estimation and scope definition. Identify and communicate technical risk early; propose mitigations and make trade-offs transparent to the Product Owner. Collaborate with the Product Owner on sprint planning to ensure technical work is appropriately sized, dependency-mapped, and sequenced. Represent engineering in architecture reviews, sprint reviews, and stakeholder discussions. Interface with the central platform engineering team on technical questions, onboarding requirements, and issue escalations. Team and Code Quality Own the code review process: set the bar, lead by example, and ensure reviews are substantive and timely. Mentor mid-level and senior engineers: support their technical growth through pairing, design discussion, and structured feedback. Build a team culture of ownership, quality, and continuous improvement; facilitate retrospective follow-through on engineering practices. Onboard new engineers to the codebase, standards, and ways of working. Collaboration Collaborate with frontend Tech Leads and other domain teams on cross-cutting API contracts and shared integration points. Work with the Product Owner on roadmap planning: provide clear technical input on feasibility, effort, and sequencing. Communicate clearly across technical and non-technical stakeholders; translate engineering complexity into business-relevant language. Required Experience & Skills 7+ years of professional software engineering experience, with at least 2 years in a technical leadership role (Tech Lead, Principal Engineer, or equivalent). Deep backend development experience: Java (Spring Boot) or equivalent, able to review, design and contribute to production code. Frontend experience: React or equivalent, able to review and contribute to production code. Capable of participating meaningfully in cross-functional technical discussions spanning frontend, BFF, and backend layers. Microservices architecture: service boundary design, API design and versioning, distributed system patterns (eventual consistency, idempotency, retry strategies). REST API design at production scale: resource modelling, contract versioning, backward compatibility, error handling conventions. Kubernetes-aware service design: designing services to run well in Kubernetes (readiness/liveness probes, graceful shutdown, resource requests, horizontal scaling). You do not need to operate the cluster. CI/CD and GitOps familiarity: understanding of pipeline-based and pull-based deployment models; able to work within and influence deployment standards. Agile delivery leadership: experience leading a team through sprint delivery, dependency management, and technical scoping in a product-led environment. Regulated industry experience: insurance, finance, or healthcare. Understands the implications of audit logging, data residency, access control, and compliance requirements on engineering decisions. Strong communicator: able to lead technical discussions, document architectural decisions clearly, and represent engineering credibly to non-technical audiences. Ways of Working Leads by example: stays close to the code and models the quality and rigour expected from the team. Collaborative and direct: shares context freely, gives clear feedback, and creates an environment where engineers can raise concerns and propose improvements. Pragmatic: balances technical correctness with delivery reality; makes trade-offs explicitly rather than silently. Comfortable with ambiguity: can scope and lead work in areas where requirements are still forming, and knows how to structure discovery to reduce uncertainty. Proactive learner: keeps up with backend and cloud-native ecosystem developments; comfortable adopting AI-assisted developer tools (e.g. GitHub Copilot, Claude Code) to improve team productivity. Nice to Have Insurance or financial services domain knowledge: understanding of claims, policy, or payment workflows. Experience integrating AI or ML services into production business logic: consuming model outputs, handling confidence thresholds, building human-in-the-loop workflows. Event-driven architecture experience: Kafka or equivalent at production scale. Cloud certifications: AZ-204 (Azure Developer), AZ-305 (Azure Solutions Architect), or equivalent. Experience leading distributed or multi-vendor engineering teams. What we offer Contract under Polish law: B2B or Umowa o Pracę Benefits such as private medical care, group insurance, Multisport card Hybrid work (at least 1 day per 2 or 3 months on-site) in Warsaw (Mokotów) Opportunity to work with excellent professionals High standards of work and focus on the quality of code New technologies in use Continuously learning and growth International team Pinball, PlayStation & much more (on-site) Join a growing team of dedicated professionals! We love to pass on the knowledge to grow excellence, speak our minds without playing politics, and just enjoy hanging around together. If you share our passions - we want to meet you! So go ahead and apply ➡️

🏢 Summary: Data Engineer role focused on designing and scaling data infrastructure, ETL pipelines, and Lakehouse architecture for a manufacturing environment supporting analytics and AI workloads. The position involves building operational data systems, ensuring data quality, and integrating industrial and manufacturing data sources. Candidates will collaborate cross-functionally and help establish scalable data architecture standards for future facility growth. 🗂️ Requirements: 8+ years of data engineering or data infrastructure experience, Experience designing data lakes or Lakehouse platforms, Experience building ETL/ELT pipelines, Strong data modeling expertise, Experience with relational databases, Strong SQL skills, Ability to document architecture and technical decisions, Experience collaborating with technical and non-technical stakeholders, U.S. Person status for export-controlled access 📃 Skills: SQL, PostgreSQL, SQLServer, ETL, ELT, Lakehouse, Python, Airflow, Prefect, dbt, InfluxDB, TimescaleDB, MQTT, DeltaLake, ApacheIceberg, AWS, Azure, GCP 🏢 Description: Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, we serve critical industries such as defense, aerospace, and automotive, powering a high-technology future. Vulcan Elements is building a team of ambitious professionals committed to Mission Focus, Technical Excellence, and Transparency. As the Data Engineer, you will design and build the data infrastructure that makes Vulcan's operational and business data useful — first at pilot scale, and then as the foundation for a 10,000 ton/year facility. You will work from architecture to implementation: evaluating and selecting platforms, designing data models and pipelines, and building the systems that collect, contextualize, and deliver data to the teams and tools that depend on it. You will collaborate closely with cross-functional stakeholders to translate operational requirements into a durable, scalable data architecture. As Vulcan grows, this role has the opportunity to expand into a team leadership position. Responsibilities Architecture & Platform Design - Design and own Vulcan's data architecture from operational data stores through ETL pipelines to the analytics and AI layer - Evaluate and select platforms for the data Lakehouse, ETL tooling, and operational databases, weighing scalability, compliance requirements, operational burden, and cost - Review, refine, and implement data architecture design documents, ensuring designs are technically sound and account for CUI and ITAR data handling requirements - Make and document key platform and design decisions with enough clarity that future team members can understand the reasoning and build on it - Ensure the architecture scales from pilot plant to full-scale facility without fundamental redesign - Apply sound engineering practices to everything you build: version control, testing, observability, and documentation, and hold those standards as the data team grows Data Pipeline & Integration - Design and build ETL pipelines that move data from operational data stores into the data Lakehouse with full contextual enrichment, making it ready for analytics and AI workloads - Build reliable ingest paths for structured data, time-series data, files, images, and other outputs from manufacturing and lab systems - Collaborate across engineering, operations, and IT to understand data flows, dependencies, and integration requirements, and translate them into pipeline and architecture decisions - Identify and eliminate manual data workflows, replacing them with monitored, reliable pipelines - Diagnose and resolve data quality issues across the stack, and build monitoring into pipelines so problems surface early Data Modeling & Quality - Define data models that support operational queries, analytical workloads, and future AI and ML applications - Own data contextualization standards ensuring every data point carries the metadata needed to make it meaningful - Contribute to schema design and payload definitions for operational data stores, working toward consistency and legibility across the organization - Support the development of reporting and visibility tools that give operations and leadership clear insight into process and quality data - Write clear technical documentation for architecture decisions, data models, pipeline designs, and operational runbooks Responsibilities and tasks outlined are not exhaustive and may change as determined by the needs of the business. Qualifications - 8+ years of experience in data engineering, data infrastructure, or a closely related technical role with a track record of owning and delivering production systems - Demonstrated experience designing and building data lakes, Lakehouses, or analytical data stores; understands the tradeoffs between platforms and can make and defend platform selection decisions - Strong experience designing and building ETL/ELT pipelines that enrich and contextualize data - Deep fluency with data modeling for both operational and analytical workloads; can design schemas that serve present needs without foreclosing future ones - Experience with relational databases (PostgreSQL, SQL Server, or similar); writes and debugs SQL confidently - Comfortable working in a fast-moving environment with a small team, making decisions with incomplete information and documenting them clearly for future colleagues - Strong communicator who can work across technical and non-technical stakeholders and translate between operational requirements and data architecture decisions - Must be a U.S. Person due to required access to U.S. export-controlled information or facilities Desired Skills - Experience with time-series databases (InfluxDB, TimescaleDB, or similar) common in industrial and IoT environments - Familiarity with industrial data concepts — historian data, process tags, OT/IT integration — and the data challenges specific to manufacturing environments - Experience working on or alongside a Unified Namespace or MQTT-based data architecture; understands how industrial messaging infrastructure relates to the data layer - Familiarity with data Lakehouse platforms and open table formats (Delta Lake, Apache Iceberg, or similar) - Experience with ETL orchestration tooling (Airflow, Prefect, dbt, or similar) - Comfort with scripting and lightweight development (Python, SQL, or similar) for pipeline development and data quality tooling - Familiarity with cloud platforms (AWS, Azure, or GCP) and experience evaluating on-premises vs. cloud tradeoffs for data infrastructure - Experience working in a controlled information environment; familiarity with the handling requirements for Controlled Unclassified Information (CUI) or export-controlled technical data under ITAR or EAR - Experience in a manufacturing, industrial, or operations-heavy environment

🏢 Summary: Data Engineer role focused on designing and scaling data infrastructure, ETL pipelines, and Lakehouse architecture for manufacturing operations supporting analytics and AI workloads. The position involves building reliable industrial data systems, defining data models, and collaborating across engineering and operations teams in a secure, compliance-driven environment. There is potential for future leadership responsibilities as the organization grows. 🗂️ Requirements: 8+ years of experience in data engineering or data infrastructure, Experience designing and building data lakes or Lakehouse platforms, Experience building ETL/ELT pipelines, Strong data modeling experience for operational and analytical workloads, Experience with relational databases, Strong SQL skills, Ability to work in fast-moving environments with small teams, Ability to communicate across technical and non-technical stakeholders, U.S. Person status for access to export-controlled information 📃 Skills: PostgreSQL, SQLServer, SQL, ETL, ELT, Lakehouse, Python, InfluxDB, TimescaleDB, MQTT, DeltaLake, Iceberg, Airflow, Prefect, dbt, AWS, Azure, GCP 🏢 Description: Vulcan Elements is manufacturing American rare-earth permanent magnets for a secure, resilient future. With a focus on national security and economic resiliency, the company serves critical industries such as defense, aerospace, and automotive. As the Data Engineer, you will design and build the data infrastructure that makes operational and business data useful — first at pilot scale, and then as the foundation for a 10,000 ton/year facility. You will work from architecture to implementation: evaluating and selecting platforms, designing data models and pipelines, and building the systems that collect, contextualize, and deliver data to the teams and tools that depend on it. You will collaborate closely with cross-functional stakeholders to translate operational requirements into a durable, scalable data architecture. As the organization grows, this role has the opportunity to expand into a team leadership position. Responsibilities Architecture & Platform Design - Design and own data architecture from operational data stores through ETL pipelines to the analytics and AI layer - Evaluate and select platforms for the data Lakehouse, ETL tooling, and operational databases, weighing scalability, compliance requirements, operational burden, and cost - Review, refine, and implement data architecture design documents, ensuring designs are technically sound and account for CUI and ITAR data handling requirements - Make and document key platform and design decisions with enough clarity that future team members can understand the reasoning and build on it - Ensure the architecture scales from pilot plant to full-scale facility without fundamental redesign - Apply sound engineering practices to everything you build: version control, testing, observability, and documentation, and hold those standards as the data team grows Data Pipeline & Integration - Design and build ETL pipelines that move data from operational data stores into the data Lakehouse with full contextual enrichment, making it ready for analytics and AI workloads - Build reliable ingest paths for structured data, time-series data, files, images, and other outputs from manufacturing and lab systems - Collaborate across engineering, operations, and IT to understand data flows, dependencies, and integration requirements, and translate them into pipeline and architecture decisions - Identify and eliminate manual data workflows, replacing them with monitored, reliable pipelines - Diagnose and resolve data quality issues across the stack, and build monitoring into pipelines so problems surface early Data Modeling & Quality - Define data models that support operational queries, analytical workloads, and future AI and ML applications - Own data contextualization standards ensuring every data point carries the metadata needed to make it meaningful - Contribute to schema design and payload definitions for operational data stores, working toward consistency and legibility across the organization - Support the development of reporting and visibility tools that give operations and leadership clear insight into process and quality data - Write clear technical documentation for architecture decisions, data models, pipeline designs, and operational runbooks Responsibilities and tasks outlined are not exhaustive and may change as determined by business needs. Qualifications - 8+ years of experience in data engineering, data infrastructure, or a closely related technical role with a track record of owning and delivering production systems - Demonstrated experience designing and building data lakes, Lakehouses, or analytical data stores; understands the tradeoffs between platforms and can make and defend platform selection decisions - Strong experience designing and building ETL/ELT pipelines that enrich and contextualize data - Deep fluency with data modeling for both operational and analytical workloads - Experience with relational databases (PostgreSQL, SQL Server, or similar) - Writes and debugs SQL confidently - Comfortable working in a fast-moving environment with a small team - Strong communicator able to work across technical and non-technical stakeholders - Must be a U.S. Person due to required access to U.S. export-controlled information or facilities Desired Skills - Experience with time-series databases (InfluxDB, TimescaleDB, or similar) - Familiarity with industrial data concepts including historian data, process tags, and OT/IT integration - Experience with Unified Namespace or MQTT-based data architecture - Familiarity with data Lakehouse platforms and open table formats (Delta Lake, Apache Iceberg, or similar) - Experience with ETL orchestration tooling (Airflow, Prefect, dbt, or similar) - Comfort with scripting and lightweight development (Python, SQL, or similar) - Familiarity with cloud platforms (AWS, Azure, or GCP) - Experience working in controlled information environments with CUI, ITAR, or EAR requirements - Experience in manufacturing, industrial, or operations-heavy environments

🏢 Summary: Senior Staff Engineer role focused on leading the design and evolution of large-scale distributed systems and data infrastructure supporting high-growth, high-throughput observability platforms. The position combines deep hands-on technical contribution with cross-team architectural leadership and long-term technical strategy ownership. You will drive high-impact initiatives such as distributed storage engines, real-time streaming infrastructure, and large-scale system re-architecture. 🗂️ Requirements: BS/MS/PhD in scientific field or equivalent experience, 10+ years backend, infrastructure, or distributed systems engineering experience, 4+ years leading complex cross-team technical initiatives, Proven design and delivery of large-scale distributed systems, Expertise in distributed storage, event streaming, networking, data infrastructure, or observability, Experience optimizing performance and reliability in high-scale systems, Ability to define architecture across multiple teams, Experience with AI coding tools and validation of AI-generated code 📃 Skills: DistributedSystems, Backend, Infrastructure, DistributedStorage, EventStreaming, Networking, DataInfrastructure, Observability, SystemDesign, Architecture, PerformanceOptimization, Reliability, Scalability, AI 🏢 Description: About Datadog: We're on a mission to build the best platform in the world for engineers to understand and scale their systems, applications, and teams. We operate at high scale—trillions of data points per day—providing always-on alerting, metrics visualization, logs, and application tracing for tens of thousands of companies. Our engineering culture values pragmatism, honesty, and simplicity to solve hard problems the right way. The Opportunity: Datadog’s Senior Staff Engineers are technical leaders operating at the forefront of large-scale systems design, building the infrastructure that will support our next five years of growth and beyond. They do this in three major ways: As individual contributors, they bring world-class technical depth to build industry-leading systems in areas such as observability data platforms, distributed query engines, and real-time event streaming at global scale. As technical leaders, they apply broad architectural perspective and deep systems thinking to align design decisions across teams and domains. They work across complex, multi-team problem spaces to define long-term technical direction, drive large-scale initiatives forward, and ensure consistent execution. As engineering stewards, they play a key role in evolving our systems and engineering culture. They actively participate in Datadog’s senior technical community, bringing external insights and internal experience to elevate engineering standards and mentor the next generation of technical leaders. Examples of projects a Senior Staff Engineer may lead include designing and launching a new distributed data storage engine capable of handling hundreds of millions of records per second, building the real-time infrastructure behind a new observability product, or re-architecting a core service to support exponential growth in throughput and complexity. What You’ll Do: Be the technical owner of multiple critical systems or architecture areas, often spanning several teams or product lines. Drive the design and delivery of high-scale, high-impact projects, from vision to execution in production. Build consensus and alignment across stakeholders on key architectural decisions and long-term strategy. Deeply investigate and optimize performance, reliability, and efficiency in large-scale distributed systems. Collaborate with Staff Engineers, Directors, and VPs across the company to shape Datadog’s technical roadmap. Guide teams through ambiguity, scaling challenges, and evolving requirements with clear technical direction. Actively mentor engineers and influence engineering culture through leadership in design reviews, technical talks, and working groups. Who You Are: You have a BS/MS/PhD in a scientific field or equivalent practical experience. You have 10+ years of backend, infrastructure, or distributed systems engineering experience. You have 4+ years of experience leading complex technical initiatives and cross-team projects.You have designed and delivered systems that are deeply technical, highly reliable, and serve critical business needs. You can navigate and make progress in ambiguous problem spaces, identifying the right problems to solve. You have exceptional communication skills, with the ability to align diverse stakeholders around a shared technical vision. You have deep expertise in one or more core systems domains—such as distributed storage, event streaming, networking, data infrastructure, or large-scale observability—and broad awareness of adjacent areas. You care about getting things right, not just getting them done, and you raise the bar for engineering quality around you. You have demonstrated ability to use AI coding tools in day-to-day workflows and validate, critique, and refine AI-generated output Bonus: You’re motivated to push the boundaries of how AI can improve software engineering best practices and contribute to building AI-enabled products Datadog values people from all walks of life. We understand not everyone will meet all the above qualifications on day one. That's okay. If you’re passionate about technology and want to grow your skills, we encourage you to apply. Benefits and Growth: Get to build tools for software engineers, just like yourself. And use the tools we build to accelerate our development. Have a lot of influence on product direction and impact on the business . Work with skilled, knowledgeable, and kind teammates who are happy to teach and learn Competitive global benefits Continuous professional development Benefits and Growth listed above may vary based on the country of your employment and the nature of your employment with Datadog. #LI-HybridDatadog offers a competitive salary and equity package, and may include variable compensation. Actual compensation is based on factors such as the candidate's skills, qualifications, and experience. In addition, Datadog offers a wide range of best in class, comprehensive and inclusive employee benefits for this role including healthcare, dental, parental planning, and mental health benefits, a 401(k) plan and match, paid time off, fitness reimbursements, and a discounted employee stock purchase plan.The reasonably estimated yearly salary for this role at Datadog is:$272,000—$340,000 USD About Datadog: Datadog (NASDAQ: DDOG) is a global SaaS business, delivering a rare combination of growth and profitability. We are on a mission to break down silos and solve complexity in the cloud age by enabling digital transformation, cloud migration, and infrastructure monitoring of our customers’ entire technology stacks. Built by engineers, for engineers, Datadog is used by organizations of all sizes across a wide range of industries. Together, we champion professional development, diversity of thought, innovation, and work excellence to empower continuous growth. Join the pack and become part of a collaborative, pragmatic, and thoughtful people-first community where we solve tough problems, take smart risks, and celebrate one another. Learn more about #DatadogLife on Instagram, LinkedIn, and Datadog Learning Center. Equal Opportunity at Datadog: Datadog is proud to offer equal employment opportunity to everyone regardless of race, color, ancestry, religion, sex, national origin, sexual orientation, age, citizenship, marital status, disability, gender identity, veteran status, and other characteristics protected by law. We also consider qualified applicants regardless of criminal histories, consistent with legal requirements. Here are our Candidate Legal Notices for your reference. Datadog endeavors to make our Careers Page accessible to all users. If you would like to contact us regarding the accessibility of our website or need assistance completing the application process, please complete this form. This form is for accommodation requests only and cannot be used to inquire about the status of applications. Privacy and AI Guidelines: Any information you submit to Datadog as part of your application will be processed in accordance with Datadog’s Applicant and Candidate Privacy Notice. For information on our AI policy, please visit Interviewing at Datadog AI Guidelines.