Associate Client Data Engineer

🏢 Summary: Entry-level Data Engineering role for a Python Developer to build and optimize data pipelines and analytical solutions under senior guidance. The position focuses on developing cloud-based data processing systems and data models while expanding expertise in modern data engineering tools. Ideal for a developer with strong Python skills aiming to transition into Data Engineering. 🗂️ Requirements: 3+ years of professional Python experience, Experience with SQL, Experience with relational databases, Ability to write clean, maintainable code, Understanding of data processing concepts, Willingness to learn cloud technologies, Analytical and problem-solving skills 📃 Skills: Python, SQL, SQLServer, PostgreSQL, MySQL, Azure, AWS, GCP, Spark, Databricks, Airflow, ETL, ELT 🏢 Description: Python Developer (Data Engineering Path) You are A passionate Python developer, eager to grow and explore the world of Data Engineering. Curious, proactive, and motivated to learn new technologies. Someone who enjoys coding, problem-solving, and wants to build data solutions that make an impact. Opportunity overview You will start your journey in Data Engineering, working closely with our senior data engineers. You’ll gradually get hands-on experience with modern data pipelines, cloud services, and analytical solutions, while leveraging your strong Python skills. Your impact zone Writing clean, maintainable Python code for data processing tasks. Learning and gradually contributing to the design and implementation of cloud-based data pipelines and data models. Collaborating with senior engineers on best practices in data engineering, including data integrity, quality, and documentation. Supporting optimization of existing analytical solutions. Growing your skills in cloud platforms (Azure / AWS / GCP) and modern data engineering tools. Qualifications & tech toolbox Very strong experience in Python (3+ years). Experience with SQL and relational databases (SQL Server, PostgreSQL, MySQL, or similar). Analytical mindset and problem-solving skills. Willingness to learn cloud technologies and modern data engineering concepts. Extra stardust for Exposure to cloud platforms (Azure / AWS / GCP). Experience with data pipelines, ETL/ELT, or big data tools. Knowledge of Apache Spark, Databricks, or Airflow. Here's why you'll love Cloudfide BENEFITS: Professional development budget – training and certification. MyBenefit cafeteria (including Multisport). Medicover medical care. Team-building events and trips. FLEXIBILITY: Work from anywhere, influence your tools, tech, and solutions. STABILITY: Long-term employment (contract of mandate or B2B). START-UP CULTURE: Open communication, creative problem solving, flat hierarchy. GROWTH: Accelerate your career by learning Data Engineering hands-on. Work on Big Data and cloud projects. COLLABORATION: Join a diverse, passionate team where every voice matters, and enjoy working with people who care about their craft. Equal opportunities CLOUDFIDE is an Equal Opportunity Employer. We celebrate diversity and are committed to creating an inclusive environment for all employees.

🏢 Summary: Lead Data Engineer role focused on driving architecture and leading a team to build scalable, secure ETL/ELT pipelines and analytics-ready data models on modern cloud platforms. The position combines hands-on engineering with technical leadership, ensuring high standards in governance, observability, and performance optimisation. You will shape data infrastructure that supports large-scale analytics across the organisation. 🗂️ Requirements: Proven experience leading data engineering or analytics engineering teams, Strong programming skills in SQL, Strong programming skills in Python, Hands-on experience with Airflow or Prefect in production, Deep practical experience with dbt, Experience with Snowflake or Databricks at scale, Strong knowledge of dimensional modelling and SCD strategies, Experience implementing data quality and governance frameworks, Experience with CI/CD and automated testing in data systems 📃 Skills: SQL, Python, Airflow, Prefect, dbt, Snowflake, Databricks, ETL, ELT, CI/CD, SCD, Dimensional, Git, IaC 🏢 Description: ABOUT THE COMPANY Our client is 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 infrastructure you lead will have real consequence across the organisation. ABOUT THE ROLE We're looking for a Lead Data Engineer to combine deep technical expertise with hands-on team leadership, guiding a team of data engineers building and maintaining ETL/ELT pipelines, data models, and governance frameworks that power analytics and reporting across the organisation. This is a technical leadership role — you'll drive architectural decisions, mentor engineers, and ensure delivery of secure, reliable, and scalable data solutions. You'll collaborate closely with stakeholders to align technical work with business objectives, champion governance and observability standards, and foster a culture of continuous improvement. The expectation is that you're equally effective in an architecture review as you are pairing with an engineer on a tricky pipeline problem. WHAT YOU'LL WORK ON Team leadership and mentorship Lead and mentor a team of data engineers, promoting collaboration, knowledge sharing, and professional growth. Set the standard for engineering quality and hold the bar consistently. Architecture and pipeline design Drive architectural decisions for ETL/ELT pipelines, orchestration frameworks (Airflow/Prefect), and transformation layers (dbt). Facilitate architecture reviews and contribute to design decisions for scalable, fault-tolerant systems. Analytics data modelling Oversee design and implementation of analytics-ready data models — dimensional schemas, SCD strategies, and semantic layers — that internal teams can build on reliably. Engineering best practices Ensure adherence to clean code, modular design, CI/CD, automated testing, and code review standards across all data engineering work. Platform optimisation Manage performance tuning and cost optimisation for Snowflake, Databricks, and related cloud data platforms at scale. Governance and observability Champion governance, observability, and compliance frameworks across all data workflows — including data quality, lineage tracking, and multi-tenant environment controls. Stakeholder communication Communicate effectively with leadership and cross-functional teams to provide updates, resolve blockers, and ensure timely delivery aligned with business objectives. WHAT WE LOOK FOR Proven technical team leadership Demonstrated experience leading data engineering or analytics-focused development teams — mentoring engineers, driving architectural decisions, and owning delivery outcomes. SQL and Python Strong programming skills in both SQL and Python, applied to production data systems at scale. ETL/ELT orchestration Hands-on experience with orchestration tools — Airflow and/or Prefect — in production pipeline environments. dbt expertise Deep practical experience with dbt for transformation workflows and analytics modelling, including testing, documentation, and modular project design. Snowflake and Databricks Familiarity with Snowflake and/or Databricks for large-scale data processing, including performance tuning and cost management. Data modelling principles Solid understanding of data modelling principles, incremental strategies, and schema design for analytics — dimensional modelling, SCDs, and semantic layer design. Governance and data quality Knowledge of data quality frameworks, lineage tracking, and governance in multi-tenant environments. Software engineering practices Familiarity with CI/CD, automated testing, and infrastructure-as-code practices applied to data systems. Communication and stakeholder management Strong communication skills with the ability to operate confidently across technical teams and business stakeholders. 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, hybrid 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: Opportunity for an experienced Python Data Engineer to build scalable data platforms and analytics systems for global financial institutions. The role focuses on designing cloud-based data pipelines, enhancing system reliability, and integrating modern AI tools into engineering workflows. You will collaborate across distributed teams to deliver high-quality solutions in enterprise data, risk analytics, and AI-driven metadata applications. 🗂️ Requirements: 4+ years of professional experience in software or data engineering using Python, Strong knowledge of data architecture, data modeling, and data warehousing, Ability to write complex SQL queries and perform advanced debugging, Experience building scalable, distributed data pipelines in cloud environments, Experience with AWS, Familiarity with event-driven architectures 📃 Skills: Python, SQL, AWS, Databricks, Spark, PySpark, Scala, Delta, Airflow, dbt, Kafka, Kubernetes, MongoDB, Grafana, Loki, Prometheus, OpenTelemetry 🏢 Description: We are partnering with top-tier global financial institutions to scale their core technology and data infrastructure. We are looking for an experienced and product-oriented Python Data Engineer to join our technology group. In this role, you will work at the intersection of cutting-edge technology and institutional finance. You will collaborate closely with data consumers, engineering teams, and business stakeholders to push the firm's technological capabilities forward. What You’ll Do: Core Responsibilities: Design, develop, and deliver high-quality, scalable Python-based solutions. Drive engineering excellence by ensuring system reliability, automating processes, and maintaining high operational standards. Actively experiment with and integrate modern AI coding tools (e.g., Copilot, Cursor) to streamline engineering workflows. Lead design discussions, mentor junior colleagues, and communicate proactively across a geo-distributed team. Depending on the specific project or team, your focus may include: Enterprise Data Platforms: Building cloud-based (AWS) pipelines for data ingestion, streaming, and cataloging. Risk & Portfolio Analytics Systems: Developing software for financial data ingress/egress, performance/exposure monitoring, and automated reconciliation. AI & Metadata Applications: Extending AI-powered semantic data access layers and internal data catalogs. What We’re Looking For: Experience: 4+ years of professional experience in software or data engineering, specifically using Python . Data Skills: Solid understanding of data architecture, modeling, and warehousing. Excellent debugging acumen and comfort writing complex SQL statements. Cloud & Architecture: Experience building scalable, distributed pipelines in a cloud environment (preferably AWS ). Familiarity with event-driven architectures. Mindset: Impact-oriented, proactive, self-starting learner who embraces engineering automation, navigates ambiguity well, and holds themselves to high ethical standards. Nice to Have: Big Data & Orchestration: Expertise in Databricks, Spark (PySpark/Scala), Delta Lake, and orchestration tools like Airflow, dbt, or Kafka. Infrastructure & Observability: Working knowledge of Kubernetes, MongoDB, and observability stacks (Grafana, Loki, Prometheus, OpenTelemetry). Offer: Competitive compensation (30k - 45k PLN) with flexible contracting options (B2B/UoP). Premium office location in the heart of Warsaw. Comprehensive private medical and dental care. Sports card and wellness benefits. Private life insurance

🏢 Summary: Senior Python Engineer role focused on building production-grade data pipelines and AI-driven systems, including LLM-based applications. The position involves designing scalable architectures, processing large datasets, and integrating modern cloud data platforms. You will work on complex, real-world data solutions used in production environments. 🗂️ Requirements: Senior-level Python experience, Strong knowledge of SQL and data processing, Experience building and maintaining data pipelines, Hands-on experience with orchestration tools (Airflow, Prefect, or Dagster), Experience with cloud data platforms (S3, BigQuery, Snowflake, or Databricks), Experience integrating LLM frameworks (LangChain or LlamaIndex), Ability to design scalable, production-ready systems 📃 Skills: Python, Pandas, NumPy, PySpark, SQL, Airflow, Prefect, Dagster, S3, BigQuery, Snowflake, Databricks, LangChain, LlamaIndex, LLM 🏢 Description: Our client is a technology consultancy that builds advanced data and AI systems for startups and enterprise companies. Their focus is on delivering production-ready solutions, especially in areas like data platforms, large-scale processing, and LLM-based applications. The Role As a Senior Python Engineer, you’ll be working on complex, data-heavy systems that go beyond prototypes. This includes designing and developing data pipelines, handling large-scale datasets, and integrating modern AI solutions (including LLMs) into real-world products. You’ll collaborate closely with experienced engineers and take part in architectural decisions, helping turn ideas into scalable, reliable systems used in production. What we’re looking for We’re looking for a strong senior-level engineer with deep experience in Python (including libraries like Pandas, NumPy, and PySpark), as well as solid knowledge of SQL and data processing. You should be comfortable working with data pipeline orchestration tools such as Airflow, Prefect, or Dagster, and have hands-on experience with modern cloud data platforms (e.g. S3, BigQuery, Snowflake, or Databricks). Experience with LLM frameworks like LangChain or LlamaIndex, especially in the context of data integration, is an important part of this role. Why join This is a great opportunity to work on real AI and data systems at scale, in a consultancy environment where engineering quality matters and you have a genuine impact on technical decisions. You’ll join a senior, low-ego team, work fully remotely from Poland, and contribute to projects for international clients.

🏢 Summary: Senior Python Backend Engineer role focused on building and scaling AI-driven services and APIs for clinical trial risk detection, with strong emphasis on AI-assisted development and LLM integration. The position involves designing robust backend systems, integrating OpenAI-based capabilities, and solving performance and architectural challenges in a data-intensive SaaS environment. You will work with modern Python frameworks and cloud-native technologies in an Agile setup. 🗂️ Requirements: 6+ years of professional software development experience, Strong production-level Python experience, Experience building and consuming REST APIs, Daily professional use of AI coding assistants (e.g. GitHub Copilot), Familiarity with Specification-Driven Development (SDD), Working knowledge of BMAD methodology for AI/ML features, Experience with SQL and/or NoSQL databases, Understanding of Agile/SCRUM methodology, Knowledge of software design and architectural patterns (SOLID, Clean Architecture), Fluent English for technical collaboration 📃 Skills: Python, FastAPI, Flask, Pyramid, REST, OpenAI, GitHub, Copilot, SDD, BMAD, SQL, MySQL, MongoDB, CosmosDB, Kubernetes, Docker, Git, Celery, Temporal, Azure, CI/CD, SOLID, Agile, SCRUM 🏢 Description: About the client: Our client is a  cutting-edge, technology focused SaaS company that provides a better way of detecting and managing risks that may impact the outcome of clinical trials. Their solutions are driven by a unique set of algorithms that interrogate clinical and operational data in real-time centrally to conveniently illuminate outliers and anomalies in data. Role description You will join a 100+ strong Engineering team driving the future of data-driven statistics, machine learning, and AI software solutions. As part of a talented group of Python developers within a dynamic, fast-scaling company, you will tackle meaningful engineering challenges — and now, you will be at the forefront of integrating AI-assisted development practices into the team's everyday workflow. Our backend engineers primarily work with Python and FastAPI, broader tech stack includes: Kubernetes, Docker, Flask, Pyramid, Git, MySQL, Mongo, Azure Cosmos Db, Celery, Temporal Responsibilities: •    Design, build and maintain robust, scalable Python services and APIs •    Integrate and extend AI/LLM capabilities using the OpenAI API and related tooling •    Accelerate development velocity through effective use of GitHub Copilot and AI-assisted code review •    Apply SDD practices to ensure features are specification-aligned and well-documented from day one •    Refactor existing codebases and perform thorough peer code reviews •    Solve complex performance bottlenecks and architectural challenges at scale •    Collaborate with Product and Design to translate end-user needs into pragmatic technical solutions •    Champion engineering best practices, clean code principles and knowledge sharing within the team Requirements: •    6+ years of professional software development experience •    Strong Python expertise — production-grade services, libraries and tooling •    Proven experience building and consuming REST-based web services •    Daily use of GitHub Copilot or equivalent AI coding assistants in a professional context •    Familiarity with SDD (Specification-Driven Development) workflows •    Working knowledge of BMAD delivery methodology for AI/ML features •    Solid understanding of SQL and/or NoSQL databases •    Clear understanding of Agile/SCRUM methodology •    Strong grasp of software design and architectural patterns (SOLID, Clean Architecture, etc.) •    Confident written and spoken English for daily collaboration in an international team Nice to have: •    Containerisation — Docker, Kubernetes or equivalent orchestration platforms •    Background in clinical data, life sciences or regulated software environments •    Contributions to open-source AI/ML projects •    Experience working with Azure, understanding of CI/CD

🏢 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: The offer is for an experienced Python Data Engineer to build scalable, cloud-based data and analytics solutions for global financial institutions. The role focuses on designing distributed data pipelines, enhancing system reliability, and developing financial and AI-driven data platforms. You will work with modern cloud and AI tools to advance enterprise data architecture and analytics capabilities. 🗂️ Requirements: 4+ years of professional experience in Python data or software engineering, Strong knowledge of data architecture, data modeling, and data warehousing, Advanced SQL skills and complex query writing, Experience building scalable distributed data pipelines, Hands-on experience with cloud environments (AWS preferred), Understanding of event-driven architectures, Experience with debugging and system reliability practices 📃 Skills: Python, SQL, AWS, Databricks, Spark, PySpark, Scala, Delta, Airflow, dbt, Kafka, Kubernetes, MongoDB, Grafana, Loki, Prometheus, OpenTelemetry 🏢 Description: We are partnering with top-tier global financial institutions to scale their core technology and data infrastructure. We are looking for an experienced and product-oriented Python Data Engineer to join our technology group. In this role, you will work at the intersection of cutting-edge technology and institutional finance. You will collaborate closely with data consumers, engineering teams, and business stakeholders to push the firm's technological capabilities forward. What You’ll Do: Core Responsibilities: Design, develop, and deliver high-quality, scalable Python-based solutions. Drive engineering excellence by ensuring system reliability, automating processes, and maintaining high operational standards. Actively experiment with and integrate modern AI coding tools (e.g., Copilot, Cursor) to streamline engineering workflows. Lead design discussions, mentor junior colleagues, and communicate proactively across a geo-distributed team. Depending on the specific project or team, your focus may include: Enterprise Data Platforms: Building cloud-based (AWS) pipelines for data ingestion, streaming, and cataloging. Risk & Portfolio Analytics Systems: Developing software for financial data ingress/egress, performance/exposure monitoring, and automated reconciliation. AI & Metadata Applications: Extending AI-powered semantic data access layers and internal data catalogs. What We’re Looking For Experience: 4+ years of professional experience in software or data engineering, specifically using Python . Data Skills: Solid understanding of data architecture, modeling, and warehousing. Excellent debugging acumen and comfort writing complex SQL statements. Cloud & Architecture: Experience building scalable, distributed pipelines in a cloud environment (preferably AWS ). Familiarity with event-driven architectures. Mindset: Impact-oriented, proactive, self-starting learner who embraces engineering automation, navigates ambiguity well, and holds themselves to high ethical standards. Nice to Have: Big Data & Orchestration: Expertise in Databricks, Spark (PySpark/Scala), Delta Lake, and orchestration tools like Airflow, dbt, or Kafka. Infrastructure & Observability: Working knowledge of Kubernetes, MongoDB, and observability stacks (Grafana, Loki, Prometheus, OpenTelemetry). Offer: Competitive compensation (30k - 45k PLN) with flexible contracting options (B2B/UoP). Premium office location in the heart of Warsaw. Comprehensive private medical and dental care. Sports card and wellness benefits. Private life insurance

🏢 Summary: Design and build a modern Data Platform from scratch for an insurance client, establishing a governed, production-ready Snowflake environment and enabling AI capabilities. The role covers full lifecycle ownership from architecture and data modelling to pipeline implementation and post-launch operations. You will develop scalable data ingestion and processing solutions while promoting best practices, automation, and CI/CD standards. 🗂️ Requirements: Hands-on experience with Python, Proven experience with data warehouse solutions (Snowflake, BigQuery or Redshift), Experience with Databricks or data lakehouse platforms, Strong expertise in data modelling and ETL/pipeline design, Experience with cloud platforms (AWS, GCP or Azure), Experience with cloud data services (S3, GCS, ABS, EMR, Dataproc, MWAA, Composer, ADF or AWS Glue), Ability to design and maintain data quality and governance standards, Experience working in Agile environments 📃 Skills: Python, SQL, Snowflake, Databricks, BigQuery, Redshift, Azure, AWS, GCP, Terraform, dbt, Spark, PowerBI, ADF, Glue, EMR, Dataproc, MWAA, Composer 🏢 Description: We are #VLteam – tech enthusiasts constantly striving for growth. The team is our foundation, that’s why we care the most about the friendly atmosphere, a lot of self-development opportunities and good working conditions. Trust and autonomy are two essential qualities that drive our performance. We simply believe in the idea of ​​“measuring outcomes, not hours”. Join us & see for yourself! About the role The majority of these roles will be at the forefront of client collaboration and building VL positions in the industry (spearheading projects). You will work closely and directly with a different specialist from the client side. Collaborate with stakeholders to define requirements, develop data pipelines and data quality metrics. You will participate in defining the requirements and architecture for the new platform, implement the solution, and remain involved in its operations and maintenance post-launch Your work will also introduce data governance and management, laying the foundation for accurate and comprehensive reporting that was previously impossible. Build data ingestion & processing pipelines. All of the above with a strong focus on the customer’s needs. Flexibility in action and the ability to overcome obstacles are highly valued in this role. View available projects: Project JetBrains Projectt scope The client is introducing Atlan as a new internal Data Catalogue solution and uses Glean as a company-wide unified search platform for thousands of employees. To ensure a smooth transition from our existing Knowledge Base and OpenMetadata setup, we need to index Atlan assets into Glean so that metadata for databases, tables, metrics, and reports is easily discoverable through search. Tech stack Python,  System & Data Integration, Kubernetes, System design, Infrastructure mindset Skills We’re looking for a Data Platform Engineer with experience in data platforms and system design at scale. We expect a track record in designing integration architectures for external systems and streamlining data migration/ingestion. As a Data Platform Engineer, you will design and implement a solution that: Periodically indexes Atlan metadata assets into Glean, runs on a configurable schedule (hourly/daily), is production-ready, observable, and maintainable by our DevOps team after handover. Moreover, ensure compliance and data governance at the appropriate level in line with the company’s standards. What we expect in general A proactive approach and flexibility in action were a must Very good command of English (written and spoken) Hands-on experience with Python Proven experience with data warehouse solutions (e.g., BigQuery, Redshift, Snowflake) Experience with Databricks or data lakehouse platforms Strong background in data modelling, data catalogue concepts, data formats, and data pipelines/ETL design, implementation and maintenance Ability to thrive in an Agile environment, collaborating with team members to solve complex problems with transparency Experience with AWS/GCP/Azure cloud services, including: GCS/S3/ABS, EMR/Dataproc, MWAA/Composer or Microsoft Fabric, ADF/AWS Glue Experience in ecosystems requiring improvements and the drive to implement best practices as a long-term process Experience with Infrastructure as Code practices, particularly Terraform, is an advantage Proactive approach Don’t worry if you don’t meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more! Apply now

🏢 Summary: Design and build a modern data platform from scratch for an insurance client, covering architecture, data ingestion, modelling, and production operations. The role focuses on establishing a governed, scalable Snowflake-based environment to enable reliable reporting and AI capabilities. You will take ownership across the full data lifecycle, from requirements definition to deployment and maintenance. 🗂️ Requirements: Hands-on experience with Python, Proven experience with data warehouse solutions (BigQuery, Redshift or Snowflake), Experience with Databricks or data lakehouse platforms, Strong expertise in data modelling and ETL/pipeline design and maintenance, Experience with AWS, GCP or Azure cloud services, Ability to design and build data ingestion and processing pipelines, Experience working in Agile environment, Understanding of data governance and data quality concepts 📃 Skills: Python, SQL, Snowflake, BigQuery, Redshift, Databricks, Azure, AWS, GCP, Terraform, dbt, PowerBI, Spark, ETL, CI/CD 🏢 Description: We are #VLteam – tech enthusiasts constantly striving for growth. The team is our foundation, that’s why we care the most about the friendly atmosphere, a lot of self-development opportunities and good working conditions. Trust and autonomy are two essential qualities that drive our performance. We simply believe in the idea of ​​“measuring outcomes, not hours”. Join us & see for yourself! About the role The majority of these roles will be at the forefront of client collaboration and building VL positions in the industry (spearheading projects). You will work closely and directly with a different specialist from the client side. Collaborate with stakeholders to define requirements, develop data pipelines and data quality metrics. You will participate in defining the requirements and architecture for the new platform, implement the solution, and remain involved in its operations and maintenance post-launch Your work will also introduce data governance and management, laying the foundation for accurate and comprehensive reporting that was previously impossible. Build data ingestion & processing pipelines. All of the above with a strong focus on the customer’s needs. Flexibility in action and the ability to overcome obstacles are highly valued in this role. View available projects: Project Data Foundation & AI Enablement Project Scope We are architecting a modern Data Platform for a fast-scaling client in the Insurance sector. Our work consolidates fragmented legacy systems, organises data from a vast number of sources, and establishes a standardised, governed, and future-proof data foundation. We aim to unlock the full value of the company’s data, enabling faster, informed decision-making and providing the backbone for business growth and AI readiness. Tech stack SQL, Python, Snowflake, dbt, Data modelling, Data quality, Power BI, Azure, Terraform Challenges The primary objective is to deliver a robust data foundation and enable AI capabilities for a client that has grown organically. The work focuses on several key areas: Establishing a production-ready, fully operational Snowflake environment and driving operational excellence. Translating complex business logic into accurate data models to ensure the platform truly reflects business reality. Integrating diverse data sources to build reliable data products and comprehensive data dictionaries. Managing the full Data Engineering and Data Science lifecycle to support production ML and AI experimentation. Taking ownership from concept to deployment. Cultivating an engineering mindset by promoting automation, CI/CD, and rigorous standards. Team We are building a small (4-6 people), agile, cross-functional team capable of delivering the complete data platform, from initial architecture to production operations. Roles involved: DevOps, Data Engineer, Snowflake Specialist, MLOps/AI Engineer, Business Analyst (BA). The team will collaborate closely with business stakeholders to ensure effective knowledge transfer and strict alignment with strategic goals. Team The team is small but highly motivated, taking on a broad scope of responsibilities as the platform is built and expanded. What we expect in general A proactive approach and flexibility in action were a must Very good command of English (written and spoken) Hands-on experience with Python Proven experience with data warehouse solutions (e.g., BigQuery, Redshift, Snowflake) Experience with Databricks or data lakehouse platforms Strong background in data modelling, data catalogue concepts, data formats, and data pipelines/ETL design, implementation and maintenance Ability to thrive in an Agile environment, collaborating with team members to solve complex problems with transparency Experience with AWS/GCP/Azure cloud services, including: GCS/S3/ABS, EMR/Dataproc, MWAA/Composer or Microsoft Fabric, ADF/AWS Glue Experience in ecosystems requiring improvements and the drive to implement best practices as a long-term process Experience with Infrastructure as Code practices, particularly Terraform, is an advantage Proactive approach Don’t worry if you don’t meet all the requirements. What matters most is your passion and willingness to develop. Apply and find out! A few perks of being with us Building tech community Flexible hybrid work model Home office reimbursement Language lessons MyBenefit points Private healthcare Training Package Virtusity / in-house training And a lot more! Apply now

🏢 Summary: Senior Data Engineer role focused on designing and building scalable serverless data ingestion pipelines in AWS within the healthcare domain. The position emphasizes strong Python engineering, cloud architecture leadership, and implementation of modern data platforms and DevOps practices. The role involves driving technical excellence and delivering reliable, high-impact data solutions in an international environment. 🗂️ Requirements: 10+ years of experience in Python programming, Strong software engineering skills in data processing, Extensive experience with AWS Cloud and Serverless Architecture, Hands-on experience with AWS Lambda, S3, and Cognito, Experience building E2E automated tests for data pipelines, Practical knowledge of Data Mesh and Medallion Architecture, Experience with Infrastructure as Code using AWS CDK or Terraform, Experience with CI/CD pipelines using GitLab or GitHub Actions, Experience with ETL/ELT processes and dbt, Experience working with GraphQL, Minimum B2 level English proficiency 📃 Skills: Python, AWS, Lambda, S3, Cognito, Boto3, DataMesh, Medallion, CDK, Terraform, GitLab, GitHubActions, ETL, ELT, dbt, GraphQL, CI/CD 🏢 Description: We are looking for a highly skilled Data Engineer to join our client in the healthcare sector. Our requirements: Technical Expertise: Python Programming: 10+ years of experience with strong Software Engineering skills focused on data processing. AWS & Serverless: Extensive experience with AWS Cloud, specifically focusing on Serverless Architecture and services (including AWS Lambda , AWS S3 Tables , and AWS Cognito ). Automated Testing: Proven experience in developing End-to-End (E2E) automated tests to ensure pipeline reliability, utilizing tools such as Boto3 for AWS resource validation. Data Concepts: Practical knowledge of Data Mesh and Medallion Architecture , along with general data processing and analysis. DevOps & IaC: Hands-on experience with Infrastructure as Code ( AWS CDK or Terraform ) and CI/CD pipelines ( GitLab pipelines or GitHub Actions ). Modern Tooling: Experience with ETL/ELT solutions, dbt , and GraphQL . Communication & Soft Skills: English Language: Minimum B2 level , enabling smooth daily technical and business communication in a global environment. Collaboration: Excellent communication skills and the ability to thrive in a collaborative, international team. Standards: A strong commitment to high standards of ethics, quality (Clean Code), and reliable delivery. Nice to have: Experience with Snowflake and SQL . Knowledge of Data Vault 2.0 modeling. Experience with Databricks . Familiarity with the Microsoft ecosystem: C# / .Net, T-SQL, SQL Server , and Azure DevOps . Experience with Star Schema database modeling. Knowledge of Descriptive Statistics. Your responsibilites: Design and build scalable Data Ingestion pipelines within the AWS cloud ecosystem. Lead technical delivery and implementation of core platform components, ensuring architectural integrity across the entire data lifecycle. Collaborate with Engineering Managers and cross-functional teams across the globe and Poland. Drive technical excellence by improving team processes, architecture standards, and engineering best practices. Support and consult with stakeholders to ensure successful delivery of high-impact, data-driven solutions. Contribute to the growth and maturity of the team’s cloud and data engineering capabilities. We offer: Challenging role within the company that creates innovative solutions. Work in international environment on demanding projects. Remote work model. Subsidized private medical care, life insurance, multisport card. Integration meetings. Employee referral program. If you have a deep expertise in Python and AWS , and building scalable Serverless data architectures is where you truly excel, this is the perfect role for you!