Senior Software Engineer

🏢 Summary: Fullstack Software Engineer II role focused on building and maintaining large-scale distributed data platforms that process petabytes of data daily. The position involves developing backend and frontend systems for customer-facing reporting and alerting services. You will work cross-functionally to design, implement, and optimize scalable solutions in a high-availability environment. 🗂️ Requirements: Solid experience with Java, Experience with Spring framework, Experience with or willingness to learn JavaScript, Experience with or willingness to learn Angular, Proficiency in API design and development, Knowledge of API security and management, Knowledge of Docker and Kubernetes, Experience with monitoring tools such as Grafana or Splunk, Strong analytical and problem-solving skills, Professional English communication skills 📃 Skills: Java, Spring, JavaScript, Angular, API, Docker, Kubernetes, Grafana, Splunk 🏢 Description: Are you passionate about distributed platforms? And solving technology problems on a large scale? Join our Data Experience team The Cloud Data Services team owns and operates systems that are used across Akamai for log delivery, analytics, reporting and alerting. These systems currently receive over 7PB/day of data and process over 5 trillion records per day. Our code helps to run one of the largest distributed systems in the world. Partner with the best Our team is collaborative, inclusive, and creative. We develop applications and services to empower our customers. In this role you will gather requirements, design, implement, and maintain services used to provide customer-facing reports and alerts. These features are vital to understand and optimize Akamai products' performance and consumption. As a Fullstack Software Engineer II you will be responsible for: Developing and integrating backend systems and frontend applications Partnering with product management, architects and other engineering teams to build features that allow customers understand their products and performance Solving complex problems in a timely and accurate manner through development, automation and systems programming Working in a cross-functional team where quality and availability are key capabilities Do what you love To be successful in this role you will: Have solid experience with Java and Spring framework Have experience with or be willing to learn Javascript and Angular Be proficient in API design, development, security and management Have knowledge of Docker/Kubernetes and monitoring tools like Grafana, Splunk Display excellent analytical, problem-solving and troubleshooting skills Demonstrate written, verbal and presentation skills in English

🏢 Summary: Senior Fullstack Software Engineer role focused on developing and maintaining a large-scale cloud management web application used by thousands of daily users. The position involves building frontend features and backend services, contributing to scalable cloud infrastructure solutions, and working in an agile environment. You will design solutions, implement new features and bug fixes, write unit tests, and integrate relevant open-source technologies. 🗂️ Requirements: Proficiency in TypeScript and frontend development, Proficiency in Python, Experience with backend service development, Understanding of algorithms, Ability to perform root cause analysis, Ability to write organized, efficient, well-documented code, Experience with unit testing, Experience working in agile development environments 📃 Skills: TypeScript, React, Python, Flask, SQLAlchemy, REST, Docker, Algorithms, UnitTesting 🏢 Description: Do you want to build the next generation of cloud services? Would you like an opportunity to effect change at a leading technology organization? Join our Compute Team! Our Team's mission is to accelerate innovation by making computing simple, scalable, accessible, and affordable. We provide the infrastructure and services that enable developers to build next-generation applications. We host hundreds of thousands of virtual machines and petabytes of data for our customers worldwide. Partner with the best Your role involves developing new features and maintaining existing ones in Cloud Manager, a web-based UI serving 10,000 daily users. Responsibilities include implementing corresponding backend service updates. The team emphasizes agile development and innovative solutions, utilizing technologies such as TypeScript, React, Python, Flask, SQLAlchemy, REST APIs, Docker, and more. As a Fullstack Senior Software Engineer, you will be responsible for: Designing - gathering all required specifications and finding an appropriate solution. Implementing bug fixes or new features. Implementing unit tests. Researching, proposing, and integrating relevant open-source projects based on product objectives. Improving internal processes and systems on a consistent basis. Do what you love To be successful in this role you will: Demonstrate proficiency in TypeScript and front-end development Be comfortable and proficient with Python Demonstrate understanding of algorithms and root cause analysis. Write organized, efficient, and well-documented code. About us At Akamai, we make life better for billions of people, trillions of times a day. Whether you're streaming live events, scrolling social media, watching your favorite series, or managing your savings, we're the engine behind the scenes. We provide the world's most distributed platform from Cloud to Edge to help the giants of the digital world work faster and stay more secure, making the internet a better experience for everyone. Our focus is simple: Cloud and Edge: Running apps closer to users for instant performance. Security : Neutralizing threats before they ever reach your data. Content Delivery : Scaling the world's biggest moments without a glitch. AI : Enabling our customers to build, secure, and scale AI apps on the world's most distributed cloud platform. At Akamai, we don't just support the internet; we power and protect it, because behind every great digital experience is a massive hidden challenge. And we're the ones who solve it. When millions of people hit play or pay, Akamai ensures it just works. Benefits at Akamai: We support your health, well-being, finances, and life beyond work. See our benefits. FlexBase adapts to your job's needs Akamai's FlexBase program is yet another way we show our commitment to providing employees with an exceptional workplace experience. It's not about telling employees where to work; it's about supporting employees to do their best work. We trust our incredible employees to work in ways that suit them best: at home, in an office, or a combination of both. Connect with us on social and see what life at Akamai is like!

🏢 Summary: Design and scale high-throughput data pipelines on cloud platforms to support advanced analytics and AI-driven products. The role focuses on building distributed data architectures in AWS and Databricks, ensuring performance, governance, and data quality. You will collaborate with AI/ML teams to deliver scalable, production-grade data solutions. 🗂️ Requirements: 3+ years of data engineering experience, Strong Python programming skills, Experience with Spark or Scala, Experience building distributed data pipelines in cloud environments, Knowledge of data modeling and data warehousing principles, Bachelor’s or Master’s degree in Computer Science or Engineering 📃 Skills: Python, Spark, Scala, AWS, Glue, EMR, Fargate, StepFunctions, Databricks, SQL, APIs, GenAI, GraphDB 🏢 Description: Data Engineer – Cloud & AI Platforms We’re looking for a Data Engineer to design and scale high-throughput data pipelines supporting advanced analytics and AI-driven products. What You’ll Do Architect and maintain distributed data pipelines in Databricks and AWS (Glue, EMR, Fargate, Step Functions) Ingest and process large volumes of structured and unstructured data (internal, market, third-party, alternative sources) Collaborate with AI/ML and engineering teams to design scalable data architectures and APIs Optimize performance and cost using Spark and cloud-native best practices Implement data governance, privacy, lineage, and access controls Build automated validation, monitoring, and data quality frameworks Evaluate emerging GenAI and data tooling to enhance platform capabilities What You Bring 3+ years of experience in data engineering Strong Python and experience with Spark or Scala Proven experience building distributed pipelines in cloud environments Solid understanding of data modeling, architecture, and warehousing principles Innovative problem-solving mindset Bachelor’s or Master’s degree in Computer Science or Engineering Nice to have: Experience with graph databases.

🏢 Summary: Software engineering role in the Data Platform team focused on building and maintaining data-driven applications, including AI-powered data access tools. The position involves developing scalable data and metadata solutions and contributing to high-impact projects across the organization. You will work across legacy and greenfield systems while leveraging modern AI-assisted development practices. 🗂️ Requirements: 3+ years of software engineering experience, Proficiency in Python, Proficiency in SQL 📃 Skills: Python, SQL, Kubernetes, Kafka, Trino, Grafana, Prometheus, CloudWatch, AWS, Copilot, Claude, Codex, AI, Cloud, Data, Metadata 🏢 Description: Join the Data Platform team to build and maintain data-driven applications, including AI-powered data access tools. Work on a mix of legacy, improved, and greenfield projects with firm-wide impact. What you’ll do: Develop and enhance solutions for data and metadata access Build reliable, scalable applications and operational processes Experiment with AI-assisted development tools and share best practices Collaborate in a distributed team, taking ownership of impactful projects What we’re looking for: 3+ years in software engineering, Python and SQL experience Experience with data systems, cloud technologies, or AI-assisted tools is a plus Product-oriented mindset, comfortable navigating ambiguity, and impact-focused Nice to have: Experience with Kubernetes, Kafka, or federated query engines (e.g., Trino) Familiarity with monitoring/observability tools like Grafana, Prometheus, CloudWatch Experience with AWS or other cloud platforms Hands-on experience with AI coding assistants (Copilot, Claude Code, Codex, etc.) Why join: High-visibility, high-impact role across the firm Shape AI-driven development and data access practices Work on cutting-edge tech in institutional finance Collaborative culture with competitive compensation

🏢 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 DevOps Engineer role focused on designing, automating, and operating AWS-based cloud infrastructure for a large-scale data and AI platform. The position centers on Infrastructure as Code, platform reliability, and enabling secure, scalable analytics workloads using Databricks and Amazon Athena. You will work hands-on with cloud and DevOps technologies in a cross-functional data environment. 🗂️ Requirements: Experience as DevOps Engineer in production cloud environments, Strong hands-on experience with AWS services, Practical experience with Databricks in cloud environments, Experience designing and managing infrastructure with Terraform, Hands-on experience with Amazon Athena, Experience with CI/CD pipelines, Experience with Git-based workflows, Knowledge of Docker and Kubernetes, Experience working with data or analytics platforms, Professional proficiency in French, Professional working proficiency in English 📃 Skills: AWS, Databricks, Terraform, Athena, Docker, Kubernetes, Airflow, MLflow, CI/CD, Git, Python, Shell, Monitoring, Logging, Cloud 🏢 Description: Client The world’s largest sporting goods retailer, with a global team of 5,000+ experts across engineering, product, data, cloud, and cybersecurity, is building the largest digital sports platform—driving innovation from product design to customer experience and sustainability. Project overview The project focuses on a cloud-native data platform supporting large-scale analytics, AI, and data processing workloads.The platform leverages technologies such as Databricks and Amazon Athena, ensuring high standards for automation, scalability, security, and operational excellence. Team You will work in a cross functional team that includes DevOps engineers, data engineers, software developers, and product stakeholders. The team collaborates closely, sharing responsibility for infrastructure quality, deployment pipelines, and platform stability. Position overview We are looking for a Senior DevOps Engineer to design, automate, and operate cloud infrastructure supporting data and AI workloads. You will focus on infrastructure automation, platform reliability, and enabling data workloads to run securely and efficiently in the cloud. The role combines hands on technical work with close collaboration across engineering and data teams. Technology stack AWS, Databricks, Terraform, AWS Cloud services, Amazon Athena, Docker, Kubernetes, Airflow, MLflow, CI CD pipelines, Git based workflows, monitoring and logging tools, scripting languages Responsibilities Design, implement, and maintain cloud infrastructure on AWS using Infrastructure as Code practices Build and manage Terraform based infrastructure to support data and analytics workloads Support and optimize Databricks environments running on AWS Configure and manage Amazon Athena for querying and analytics use cases Develop, maintain, and improve CI CD pipelines for infrastructure and platform components Monitor platform performance, availability, and security and address operational issues Collaborate with data engineers and developers to translate platform requirements into technical solutions Troubleshoot infrastructure and deployment issues and perform root cause analysis Contribute to platform standardization, documentation, and continuous improvement initiatives Requirements Experience working as a DevOps Engineer in production cloud environments Strong hands on experience with AWS services Practical experience using Databricks in a cloud based environment Experience designing and managing infrastructure with Terraform Hands on experience with Amazon Athena Experience working with CI CD pipelines and Git based workflows Knowledge of containerization (Docker, Kubernetes) Experience working in data or analytics platforms Ability to communicate effectively in French in a professional context Professional working proficiency in English Nice to have Experience with data engineering or analytics platforms Exposure to multi-cloud architectures (AWS & GCP) Experience in enterprise-scale environments Strong interest in AI / Generative AI technologies Scripting or programming experience with Python, Shell, or similar languages Experience working in large scale or enterprise environments What We Offer: Vacation days : Up to 26 business days per year. 10 illness/special days off per year (fully paid, no medical papers needed) for all contract types Health and life insurance (Luxmed) MyBenefit platform with Multisport option Internal psychological support service English language classes from the first working day Access to external learning platforms : O’Reilly, LinkedIn Learning, Udemy, and a wide catalog of diverse internal training Flexible workplace : work from the office, from home, or choose a hybrid option Tech Skills Mentoring Program Opportunities to develop as a public speaker, mentor, or technical interviewer Fully paid idle (bench) when not involved in a project Certification reimbursement (AWS, GCP, Microsoft, etc.)

🏢 Summary: The offer is for an experienced Big Data Engineer to design and maintain a high-performance data analytics platform processing petabyte-scale datasets. The role focuses on building scalable data pipelines, optimizing distributed systems, and delivering real-time reporting solutions using modern Big Data technologies. You will work on cutting-edge data engineering challenges in a large-scale, global environment. 🗂️ Requirements: Bachelor’s or Master’s degree in Computer Science, Engineering, or related field, Proven experience as Data Engineer or similar role, Strong proficiency in Spark, HDFS, Airflow, Strong programming skills in Scala, Experience with data pipeline architecture and implementation, Experience building continuous reporting systems and dashboards, Knowledge of data warehousing concepts, Ability to optimize and tune distributed data systems, Experience ensuring data quality and integrity, Ability to troubleshoot complex data pipeline issues 📃 Skills: Scala, Spark, HDFS, Airflow, Gradle, Kubernetes, BigData, ETL, DataWarehousing, Dashboards 🏢 Description: Our customer is one of the world’s largest technology companies based in Silicon Valley with operations all over the world. In this project, we are working on the bleeding edge of Big Data technology to develop a high-performance data analytics platform, which handles petabytes datasets. We are looking for an experienced Big Data Engineer. Responsibilities: Design, develop, and maintain robust data pipelines using: Scala, Spark, HDFS, Gradle, Kubernetes and Airflow Implement scalable and efficient data workflows to support data ingestion, processing, and analysis. Develop and maintain continuous reporting systems and dashboards to provide real-time insights. Collaborate with cross-functional teams to understand data requirements and deliver data solutions. Optimize and tune data processing systems for performance and reliability. Ensure data quality and integrity throughout the data lifecycle. Troubleshoot and resolve data processing and pipeline issues. Stay up-to-date with industry trends and emerging technologies to drive innovation in data engineering. Requirements : Bachelor's or Master's degree in Computer Science, Engineering, or a related field. Proven experience as a Data Engineer or in a similar role. Strong proficiency in Spark, HDFS, Airflow Solid programming skills in Scala. Experience with data pipeline architecture and implementation. Expertise in building and maintaining continuous reporting systems and dashboards. Familiarity with data warehousing concepts and technologies. Strong problem-solving skills and attention to detail. Excellent communication and collaboration skills. Ability to work independently and in a remote team environment. We offer: Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office About us: Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI, supported by profound expertise and ongoing investment in data, analytics, cloud & DevOps, application modernization and customer experience. Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

🏢 Summary: Senior Big Data Developer role focused on full ownership of a modern AWS-based data platform, including architecture, infrastructure, and performance optimization. The position involves building end-to-end data pipelines, managing cloud infrastructure as code, and implementing a scalable data lakehouse using advanced big data frameworks. The role emphasizes deep AWS expertise, Spark-based processing, and workflow orchestration in a high-performance environment. 🗂️ Requirements: Expert-level Core Python proficiency, Strong experience with PySpark and Apache Spark, Extensive hands-on experience across AWS services (S3, Lambda, EMR, Redshift, Glue, VPC, ECS, ECR, SQS), Strong proficiency in AWS CDK and AWS CLI, Experience designing and managing Airflow workflows, Experience with Apache Iceberg table format, Advanced SQL skills, Experience with REST API integration, Proficiency in Bash scripting, Experience with infrastructure-as-code and cloud security (IAM, KMS, Secrets Manager) 📃 Skills: Python, PySpark, Spark, AWS, S3, Lambda, EMR, Redshift, Glue, VPC, ECS, ECR, SQS, CDK, CLI, Airflow, StepFunctions, Iceberg, SQL, REST, Bash, IAM, KMS, Athena, RDS, Pydantic, Alembic 🏢 Description: We are looking for a Senior Big Data Developer to join our team and take full ownership of a modern, high-performance data platform. Essential functions End-to-End Data Engineering: Design and build complex ingestion and transformation pipelines using Python (PySpark) and Apache Spark. Cloud Infrastructure Ownership: Take full responsibility for our AWS environment, utilizing AWS CDK to manage everything from VPCs and Load Balancers to EMR clusters and ECS containers. Orchestration & Workflow: Develop and manage sophisticated data workflows using Airflow (MWAA) and AWS Step Functions. Modern Data Lakehouse: Implement and optimize storage layers using Apache Iceberg and Redshift, adhering to Medallion Architecture and advanced design patterns. Infrastructure-as-Code: Architect self-healing and scalable infrastructure through AWS CDK, ensuring security and compliance via IAM, KMS, and Secret Manager. Performance Tuning: Monitor and optimize query performance across Athena, Glue, and RDS to ensure low-latency data access. Qualifications Python Mastery: Expert-level proficiency in Core Python, including experience with Pydantic and Alembic. Deep AWS Expertise: Extensive hands-on experience across the AWS ecosystem (S3, Lambda, EMR, Redshift, Glue, VPC, ECS, ECR, SQS). IaC & Automation: Strong proficiency in AWS CDK and the AWS CLI; ability to manage your own cloud infrastructure independently. Big Data Frameworks: Proven experience with Apache Spark (PySpark) and modern table formats like Apache Iceberg. Data Orchestration: Hands-on experience with Airflow for managing complex data lifecycles. SQL & APIs: Advanced SQL skills and a solid understanding of REST API integration. Scripting: Proficiency in Bash for system-level automation. Would be a plus Backend Engineering Background: Prior experience in software engineering (beyond just data) is a major plus, specifically building scalable microservices. Familiarity with distributed design patterns and data governance. We offer Opportunity to work on bleeding-edge projects Work with a highly motivated and dedicated team Competitive salary Flexible schedule Benefits package - medical insurance, sports Corporate social events Professional development opportunities Well-equipped office About us Grid Dynamics (NASDAQ: GDYN) is a leading provider of technology consulting, platform and product engineering, AI, and advanced analytics services. Fusing technical vision with business acumen, we solve the most pressing technical challenges and enable positive business outcomes for enterprise companies undergoing business transformation. A key differentiator for Grid Dynamics is our 8 years of experience and leadership in enterprise AI , supported by profound expertise and ongoing investment in data , analytics , cloud & DevOps , application modernization and customer experience . Founded in 2006, Grid Dynamics is headquartered in Silicon Valley with offices across the Americas, Europe, and India.

🏢 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: Role focused on designing and building distributed, highly parallelized Big Data pipelines to process massive structured and unstructured data in near real-time. Involves using Spark and Hadoop to transform and enrich data for search, visualization, and advanced analytics, while collaborating with analysts and QA. Emphasis on automation, reliability, and continuous delivery on Big Data platforms. 🗂️ Requirements: Experience building distributed data processing pipelines, Experience processing large-scale structured and unstructured data, Strong knowledge of Apache Spark, Hands-on experience with Hadoop ecosystem, Ability to develop data transformation and enrichment workflows, Experience with real-time or near real-time data processing, Experience with process automation, Experience working with QA teams 📃 Skills: Spark, Hadoop, BigData, ETL, DistributedSystems, DataProcessing, Automation, Analytics 🏢 Description: · Building distributed and highly parallelized Big data processing pipeline which process massive amount of data (both structured and unstructured data) in near real-time · Leveraging Spark to enrich and transform corporate data to enable searching, data visualization, and advanced analytics · Working closely with analysts and business stakeholders to develop analytics models · Continuous delivering on Hadoop and other Big Data Platforms · Automating processes where possible and are repeatable and reliable · Working closely with QA team