Data Engineer III

🏢 Summary: Full-time Data Engineer III role focused on building and optimizing large-scale healthcare data infrastructure and production-grade data pipelines within AWS. The position requires deep expertise in SQL and Python, experience with Airflow and DBT, and use of AI tooling to improve engineering workflows and data platform efficiency. Compensation includes salary, bonus, and equity. 🗂️ Requirements: Bachelor's degree in Computer Science, Engineering, or related field or equivalent experience, 5+ years of experience as a Data Engineer, Expert-level SQL, Strong Python proficiency, Experience with AWS in production environments, Experience building and scaling large-scale data infrastructure, Experience with Apache Airflow, Experience maintaining ELT processes with DBT, Ability to use AI tools and LLM-assisted development workflows 📃 Skills: SQL, Python, AWS, Lambda, Glue, Fargate, Airflow, DBT, PostgreSQL, Redshift, ELT, Terraform, CloudFormation, GitHubCopilot, LLM 🏢 Description: ABOUT THE ROLE: We are seeking a full-time Data Engineer III to join our team and define and deploy mission-critical data infrastructure. The primary responsibility for this role is defining the processing of large healthcare datasets, which enable insights that improve the health of more than 3 million members. In this role, you will bring deep expertise in SQL and Python to build and maintain production-grade data pipelines within the AWS ecosystem and leverage AI tooling to augment workflow, accelerate development, and continuously improve the reliability and efficiency of the data platform. WHAT YOU'LL DO: - Write production-level Python and SQL code, with a strong emphasis on complex SQL for data transformation, pipeline logic, and analytical query optimization. - Leverage AI tools and large language models to augment engineering workflows from code generation and query optimization to automated testing, documentation, and process improvement. - Utilize AWS services such as Lambda, Glue, and Fargate for scalable data operations. - Build and maintain large-scale data pipelines using Apache Airflow. - Oversee the maintenance of Extract, Load, and Transform (ELT) processes using DBT. - Design optimized database schemas in PostgreSQL and Amazon Redshift to ensure data integrity and high performance. - Identify opportunities to apply AI-driven automation to repetitive or high-volume data engineering tasks, reducing manual effort and improving throughput. WHO YOU ARE: - Bachelor's degree in Computer Science, Engineering, or a related field, or equivalent work experience. - 5+ years of industry experience as a data engineer. - Expert-level SQL skills, including query optimization, schema design, and working with large-scale datasets in production environments. - Strong Python proficiency with experience writing and maintaining production-grade code. - Skilled in working with AWS systems in a production environment. - Experienced in scaling data infrastructure to handle high volumes of data. - Demonstrated ability to incorporate AI tools (e.g. GitHub Copilot, LLM-assisted development) into an engineering workflow to improve speed and quality. EXTRA CREDIT: - Experience working with medical and pharmacy claims data. - Knowledge in deploying applications using serverless architecture. - Hands-on experience managing AWS services such as Lambda, Glue, and Fargate. - Experience with Master Data Management and data governance. - Familiarity with infrastructure-as-code services such as Terraform or CloudFormation. - Experience using AI tooling to automate data quality checks, generate pipeline scaffolding, or accelerate ETL development. COMPENSATION: $125,000 - $150,000 annually, in addition to bonus and equity. Compensation offered will be determined by geographic location, experience, and qualifications.

🏢 Summary: Analytics Engineer role focused on building scalable healthcare data products and analytics-ready models for PBM and care navigation operations using dbt, SQL, and cloud data platforms. The position involves developing reliable data pipelines, implementing data quality frameworks, and enabling self-service and AI-assisted analytics capabilities. Candidates will collaborate across technical and business teams to create maintainable, auditable, and high-performance data solutions. 🗂️ Requirements: 3+ years experience in analytics engineering, data modeling, data warehousing or related field, Strong experience with dbt, Advanced SQL proficiency, Experience developing data transformations and workflows using Python, Understanding of dimensional modeling and scalable data architecture, Experience building production-grade data models and data quality tests, Experience with cloud data warehouses, Experience orchestrating and monitoring data pipelines, Experience working in cloud environments, Familiarity with Git and CI/CD workflows, Knowledge of testing and deployment workflows, Strong problem-solving and analytical skills, Effective communication and collaboration skills 📃 Skills: dbt, SQL, Python, Redshift, Snowflake, BigQuery, Airflow, Dagster, AWS, Git, CI/CD, MCP, Claude 🏢 Description: ABOUT THE ROLE: We are seeking an Analytics Engineer to join our Analytics Platform team to design, build and maintain scalable data products that power our Pharmacy Benefit Management (PBM) and Care Navigation organizations. In this role, you will be responsible for transforming complex healthcare data into trusted, analytics ready datasets that support operational reporting, client insights, financial analysis and strategic decision-making. You will partner closely with Analytics, Data Engineering, Clinical, Client Success, Product and Operations teams to build reliable and scalable data models within our data foundry. This role is ideal for someone who is passionate about analytics engineering, has strong dbt expertise, enjoys solving complex healthcare data challenges and thinks beyond individual requests to build reusable and maintainable systems. You will play a key role in shaping the future of our data platform by developing high-quality data products, establishing engineering best practices, and helping the organization evolve toward AI-enabled and self-service analytics capabilities. WHAT YOU'LL DO: • Apply hands on analytics and data expertise to solve complex, fast moving financial and operational problems in the health tech space • Design, develop and maintain scalable, analytics ready data models (primarily in dbt) that power a PBM and care navigation data ecosystem that support client reporting, performance analytics, operational analysis and self-service business intelligence • Translate complex pharmacy benefits and healthcare navigation workflows, business rules and operational processes into transparent, maintainable, and auditable data models • Partner closely with data and analytics engineers, data analysts and business stakeholders to deliver reliable data products that can be leveraged across multiple use cases and continuously optimize data models and warehouse performance to support large-scale PBM/care navigation datasets and growing business needs • Implement automated data quality checks, testing frameworks and reconciliation processes to ensure data reliability • Establish documentation standards, lineage and analytics engineering guardrails that promote transparency and auditability • Contribute to engineering best practices including version control, CI/CD, incremental models, code reviews, and observability • Contribute to data governance by establishing modeling standards, documentation and guardrails that support auditability, explainability and long term maintainability • Build data foundations that enable future agentic analytics engineering use cases, including self-service analytics and AI-assisted insight generation • Stay informed on emerging technologies and identify opportunities to incorporate AI into analytics engineering workflows WHO YOU ARE: • 3+ years of experience in analytics engineering, data modeling, data warehousing or a related field • Strong experience with dbt and modern analytics engineering best practices (required) • Advanced SQL proficiency and experience developing data transformations and workflows using Python • Strong understanding of dimensional modeling, medallion architecture, semantic modeling and scalable data architecture principles • Experience building and maintaining production-grade data models, data quality tests, and documentation • Experience working with cloud data warehouses such as Amazon Redshift, Snowflake, or BigQuery • Experience orchestrating and monitoring data pipelines using tools such as Apache Airflow, Dagster etc. • Experience working within cloud environments, preferably AWS • Familiarity with software engineering practices including Git, CI/CD, pull request reviews, testing, and deployment workflows • Interest or experience in leveraging AI-enabled development workflows and analytics tooling, including experience with or willingness to learn technologies such as Claude, Claude Skills, Model Context Protocol (MCP) and AI assisted engineering practices • Strong problem-solving and analytical skills with a focus on building scalable, maintainable data solutions • Effective communication skills and the ability to collaborate with both technical and non-technical stakeholders COMPENSATION: $100,000 - $140,000 annually, in addition to bonus and equity. Compensation offered will be determined by geographic location, experience, and qualifications.

🏢 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!

🏢 Summary: Senior Data Platform Engineer role focused on designing, building, and operating scalable, highly available data persistence systems for distributed services. The position combines backend engineering, platform reliability, and cloud-native data infrastructure to improve performance, scalability, and observability of global data ecosystems. Hybrid work model with competitive salary and comprehensive benefits. 🗂️ Requirements: 5+ years of software engineering experience in production systems, Strong backend engineering skills (Python, Java, or Kotlin), Experience with large-scale, data-intensive systems, Solid understanding of distributed systems fundamentals, Experience in cloud environments (AWS preferred), Experience with relational or NoSQL databases, Hands-on experience with large-scale data pipelines, Experience with event-driven or streaming architectures, Understanding of end-to-end data flow (ingestion, transformation, storage, access), Experience designing scalable and reliable data systems 📃 Skills: Python, Java, Kotlin, Kafka, Spark, PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch, AWS, Terraform, ETL 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Data Platform Engineer to join the Datastores team. This team is responsible for building and operating the core data persistence layer used by application services across the organization. In this role, you will design and improve the systems that store, access, and scale critical data across distributed services. It is a hands-on engineering position where you will work at the intersection of backend engineering, platform reliability, and cloud-native data infrastructure. Your work will directly influence the scalability, performance, and reliability of the company’s global data ecosystem. Key takeaways: Stack: Python, Kafka, Spark, PostgreSQL, AWS Salary: 23.000 - 30 000 PLN gross per month on Employment Contract Working model: hybrid - 3x weekly from the office Location: ul. Grzybowska 60, Warsaw Recruitment process: A call with Motife recruiter (30 min) Coding Interview (1h) Interview panel: architecture & system design discussion; Hiring Manager meeting (up to 2h in total) Responsibilities: Data Infrastructure Engineering Design, build, and operate backend systems that rely on scalable and highly available data persistence layers. Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability. Improve the reliability and performance of production datastores used by critical services. Data Performance & Optimization Partner with service teams to improve database schema design, query performance, and data modelling. Optimize data access patterns and indexing strategies for relational and NoSQL databases. Support teams in designing systems that scale efficiently under high load. Developer Experience & Platform Tooling Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers. Contribute to infrastructure automation using tools such as Terraform and internal developer platforms. Improve observability and operational insight into datastore performance and reliability. Platform Reliability & Observability Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems. Develop autoscaling and performance optimization strategies for critical data infrastructure. Support operational excellence by reducing manual processes and improving system resilience. Requirements: Technical Expertise 5+ years of experience in software engineering, building and operating production systems Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin) Experience working with large-scale, data-intensive systems Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency) Experience working in cloud environments (preferably AWS) Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch) Data Systems & Architecture Hands-on experience with large-scale data pipelines and data processing systems Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows) Understanding of end-to-end data flow: ingestion (how data enters the system) transformation (how it is processed) storage & access (how other services consume it) Experience designing systems where data performance, scalability, and reliability are critical Collaboration & Engineering Mindset Ability to work cross-functionally with service teams to improve system design and data access patterns. Strong problem-solving skills with a focus on performance, scalability, and reliability. Clear communication skills and a collaborative engineering approach. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If you’re excited about building developer platforms that scale, empower teams, and set new standards for engineering excellence, we’d love to hear from you. Apply via our careers page and please submit your CV in English .

🏢 Summary: Senior Data Platform Engineer role focused on designing, building, and operating scalable, highly available data persistence systems for distributed services. The position combines backend engineering, cloud-native infrastructure, and data platform reliability to improve performance and scalability of global data systems. It is a hands-on role working with large-scale data pipelines, streaming, and cloud environments. 🗂️ Requirements: 5+ years of software engineering experience in production systems, Strong backend programming skills in Python, Java, or Kotlin, Experience with large-scale, data-intensive systems, Solid understanding of distributed systems fundamentals, Experience with cloud environments, preferably AWS, Experience with relational or NoSQL databases, Hands-on experience with large-scale data pipelines, Experience with event-driven architectures or streaming systems, Understanding of end-to-end data flow and data modeling, Experience designing scalable and reliable data systems, Experience with infrastructure automation tools 📃 Skills: Python, Java, Kotlin, Kafka, Spark, PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch, AWS, Terraform, ETL 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Data Platform Engineer to join the Datastores team. This team is responsible for building and operating the core data persistence layer used by application services across the organization. In this role, you will design and improve the systems that store, access, and scale critical data across distributed services. It is a hands-on engineering position where you will work at the intersection of backend engineering, platform reliability, and cloud-native data infrastructure. Your work will directly influence the scalability, performance, and reliability of the company’s global data ecosystem. Key takeaways: Stack: Python, Kafka, Spark, PostgreSQL, AWS Salary: 23.000 - 30 000 PLN gross per month on Employment Contract Working model: hybrid - 3x weekly from the office Location: ul. Grzybowska 60, Warsaw Recruitment process: A call with Motife recruiter (30 min) Coding Interview (1h) Interview panel: architecture & system design discussion; Hiring Manager meeting (up to 2h in total) Responsibilities: Data Infrastructure Engineering Design, build, and operate backend systems that rely on scalable and highly available data persistence layers. Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability. Improve the reliability and performance of production datastores used by critical services. Data Performance & Optimization Partner with service teams to improve database schema design, query performance, and data modelling. Optimize data access patterns and indexing strategies for relational and NoSQL databases. Support teams in designing systems that scale efficiently under high load. Developer Experience & Platform Tooling Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers. Contribute to infrastructure automation using tools such as Terraform and internal developer platforms. Improve observability and operational insight into datastore performance and reliability. Platform Reliability & Observability Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems. Develop autoscaling and performance optimization strategies for critical data infrastructure. Support operational excellence by reducing manual processes and improving system resilience. Requirements: Technical Expertise 5+ years of experience in software engineering, building and operating production systems Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin) Experience working with large-scale, data-intensive systems Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency) Experience working in cloud environments (preferably AWS) Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch) Data Systems & Architecture Hands-on experience with large-scale data pipelines and data processing systems Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows) Understanding of end-to-end data flow: ingestion (how data enters the system) transformation (how it is processed) storage & access (how other services consume it) Experience designing systems where data performance, scalability, and reliability are critical Collaboration & Engineering Mindset Ability to work cross-functionally with service teams to improve system design and data access patterns. Strong problem-solving skills with a focus on performance, scalability, and reliability. Clear communication skills and a collaborative engineering approach. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If you’re excited about building developer platforms that scale, empower teams, and set new standards for engineering excellence, we’d love to hear from you. Apply via our careers page and please submit your CV in English .

🏢 Summary: Senior Data Platform Engineer role focused on designing, building, and operating scalable, highly available data persistence systems for distributed services. The position combines backend engineering, cloud-native infrastructure, and data platform reliability to support global, data-intensive applications. The engineer will enhance performance, scalability, and observability of production datastores across a multi-region environment. 🗂️ Requirements: 5+ years of software engineering experience in production systems, Strong backend programming skills (Python, Java, or Kotlin), Experience with large-scale, data-intensive systems, Solid understanding of distributed systems fundamentals, Experience with cloud environments, preferably AWS, Hands-on experience with relational or NoSQL databases, Experience with large-scale data pipelines and data processing systems, Experience with event-driven architectures or streaming/batch processing, Understanding of end-to-end data flow (ingestion, transformation, storage, access), Experience designing scalable, high-performance, reliable data systems 📃 Skills: Python, Java, Kotlin, Kafka, Spark, PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch, AWS, Terraform, ETL 🏢 Description: We are hiring on behalf of our client, a global innovator in fitness and wellness technology. Their mission is to empower people to live fit, strong, long, and happy lives by delivering integrated experiences to millions of members anytime, anywhere. We are looking for a Senior Data Platform Engineer to join the Datastores team. This team is responsible for building and operating the core data persistence layer used by application services across the organization. In this role, you will design and improve the systems that store, access, and scale critical data across distributed services. It is a hands-on engineering position where you will work at the intersection of backend engineering, platform reliability, and cloud-native data infrastructure. Your work will directly influence the scalability, performance, and reliability of the company’s global data ecosystem. Key takeaways: Stack: Python, Kafka, Spark, PostgreSQL, AWS Salary: 23.000 - 30 000 PLN gross per month on Employment Contract Working model: hybrid - 3x weekly from the office Location: ul. Grzybowska 60, Warsaw Recruitment process: A call with Motife recruiter (30 min) Coding Interview (1h) Interview panel: architecture & system design discussion; Hiring Manager meeting (up to 2h in total) Responsibilities: Data Infrastructure Engineering Design, build, and operate backend systems that rely on scalable and highly available data persistence layers. Contribute to architectural decisions around distributed data systems, multi-region persistence, and global scalability. Improve the reliability and performance of production datastores used by critical services. Data Performance & Optimization Partner with service teams to improve database schema design, query performance, and data modelling. Optimize data access patterns and indexing strategies for relational and NoSQL databases. Support teams in designing systems that scale efficiently under high load. Developer Experience & Platform Tooling Build and maintain self-service tooling that enables engineers to provision and manage databases and caching layers. Contribute to infrastructure automation using tools such as Terraform and internal developer platforms. Improve observability and operational insight into datastore performance and reliability. Platform Reliability & Observability Implement monitoring, metrics, and tracing strategies to improve visibility into production data systems. Develop autoscaling and performance optimization strategies for critical data infrastructure. Support operational excellence by reducing manual processes and improving system resilience. Requirements: Technical Expertise 5+ years of experience in software engineering, building and operating production systems Strong backend engineering fundamentals (e.g. Python, Java, or Kotlin) Experience working with large-scale, data-intensive systems Solid understanding of distributed systems fundamentals (e.g. scalability, latency, reliability, data consistency) Experience working in cloud environments (preferably AWS) Familiarity with relational or NoSQL databases (e.g. PostgreSQL, MySQL, DynamoDB, Redis, Elasticsearch) Data Systems & Architecture Hands-on experience with large-scale data pipelines and data processing systems Exposure to event-driven architectures, streaming or batch processing (e.g. Kafka, Spark, ETL workflows) Understanding of end-to-end data flow: ingestion (how data enters the system) transformation (how it is processed) storage & access (how other services consume it) Experience designing systems where data performance, scalability, and reliability are critical Collaboration & Engineering Mindset Ability to work cross-functionally with service teams to improve system design and data access patterns. Strong problem-solving skills with a focus on performance, scalability, and reliability. Clear communication skills and a collaborative engineering approach. What we offer: 100% paid medical care Multisport Creative tax (KUP) Home office allowance MacBook Pro Apply now If you’re excited about building developer platforms that scale, empower teams, and set new standards for engineering excellence, we’d love to hear from you. Apply via our careers page and please submit your CV in English .

🏢 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: The offer is for a Data Engineer role focused on designing and optimizing cloud-based data solutions in AWS and Azure to support healthcare data projects. The position involves building scalable ETL pipelines, improving data architecture, and contributing to cloud migration initiatives. The role is based in Warsaw within a growing data engineering team working on advanced data platforms. 🗂️ Requirements: 3–6 years of experience in data engineering, Experience with ETL, data integration and data modeling, Strong knowledge of AWS or Azure cloud services, Experience with Azure Databricks, Data Lake, Data Factory, Glue, Synapse, Snowflake or AWS Redshift, Proficiency in Python, Proficiency in SQL, Familiarity with CI/CD tools, Understanding of DevOps concepts, Knowledge of data integration and data quality principles, English proficiency at B2 level or higher 📃 Skills: AWS, Azure, Databricks, DataLake, DataFactory, Glue, Synapse, Snowflake, Redshift, Python, SQL, ETL, CI/CD, DevOps 🏢 Description: Choosing Capgemini means choosing a company where you will be empowered to shape your career in the way you’d like, where you’ll be supported and inspired by a collaborative community of colleagues around the world, and where you’ll be able to reimagine what’s possible. Join us and help the world’s leading organizations unlock the value of technology and build a more sustainable, more inclusive world. Your role Join Capgemini and help shape the future of healthcare data. As a Data Engineer, you’ll design and optimize cloud-based data solutions that power critical insights and improve patient outcomes. Your work will directly contribute to projects that make a real difference. Join Our Growing Data Engineering Teams in Warsaw! Capgemini is expanding its database-focused teams located in this office. We are looking for talented Data Engineers to join us in shaping innovative solutions for our clients. If you are passionate about data, cloud technologies, and building scalable systems, we have exciting opportunities for you! As part of our team, you will work on cutting-edge projects, collaborate with experts, and contribute to delivering high-quality data solutions in a dynamic and supportive environment. Ready to take the next step in your career? Apply now and become part of our growing data engineering community in Warsaw! Your tasks Design, implement, and maintain data processing solutions in AWS and Azure environments. Build and optimize ETL pipelines and data integration workflows. Develop and maintain data pipelines using Azure Data Factory, Synapse, and Databricks. Ensure data quality, security, and compliance standards across all solutions. Collaborate with analysts, business stakeholders, and cross-functional teams to deliver reliable data solutions. Support data architecture improvements and cloud migration projects. Your profile 3-6 years of experience in data engineering (ETL, integration, modeling). Strong knowledge of AWS or Azure, or both services (Azure Databrick, Data Lake, Data Factory, Glue, Synapse, Snowflake, AWS Redshift). Proficiency in Python and SQL. Familiarity with CI/CD tools and basic DevOps concepts. Understanding of data integration and data quality principles. English at B2 level or higher. Healthcare or fintech/pharma project experience is a plus. What You'll love about working here Practical benefits: private medical care with Medicover with additional packages (e.g., dental, senior care, oncology) available on preferential terms, life insurance and 40+ options on our NAIS benefit platform, including Netflix, Spotify or Sports card. Access to over 70 training tracks with certification opportunities (e.g., GenAI, Excel, Business Analysis, Project Management) on our NEXT platform. Dive into a world of knowledge with free access to Education First languages platform, TED Talks and Udemy Business materials and trainings. Enjoy hybrid working model that fits your life - after completing onboarding, connect work from a modern office with ergonomic work from home, thanks to home office package (including laptop, monitor, and chair). Ask your recruiter about the details. Get to know us Capgemini is committed to diversity and inclusion, ensuring fairness in all employment practices. We evaluate individuals based on qualifications and performance, not personal characteristics, striving to create a workplace where everyone can succeed and feel valued. Do you want to get to know us better? Check our Instagram — @capgeminipl or visit our Facebook profile — Capgemini Polska . You can also find us on YouTube . About Capgemini Capgemini is an AI-powered global business and technology transformation partner, delivering tangible business value. We imagine the future of organizations and make it real with AI, technology and people. With our strong heritage of nearly 60 years, we are a responsible and diverse group of over 420,000 team members in more than 50 countries. We deliver end-to-end services and solutions with our deep industry expertise and strong partner ecosystem, leveraging our capabilities across strategy, technology, design, engineering and business operations. The Group reported 2025 global revenues of €22.5 billion.

🏢 Summary: Full-time Data Engineer role focused on leading the data lifecycle strategy, transforming and governing large-scale datasets, and integrating advanced analytics and machine learning models into a production data infrastructure. The position involves building high-quality, traceable data architectures and enabling real-time, data-driven decisions for government-related markets. Based in Pittsburgh, PA, with limited travel requirements. 🗂️ Requirements: U.S. Citizenship, Bachelor’s degree in Computer Science, Mathematics, or related technical field, 3–5 years of experience programmatically transforming data, Experience with RDBMS, Advanced SQL programming skills, Proficiency with CSV, XML, and JSON data formats, Strong analytical skills and attention to detail, Ability to work independently with minimal supervision 📃 Skills: SQL, RDBMS, CSV, XML, JSON, AWS, DataModeling, DataGovernance, ETL, MachineLearning 🏢 Description: Company Description Air is the leader in Enterprise Readiness. Our mission is to establish readiness as a real-time condition that is continuously achieved. Today, a dangerous Readiness Gap exists between what the front line needs and what is delivered. Our AI-native platform, Air Enterprise Readiness, aligns development, production, delivery, and sustainment into one coordinated execution system for government agencies and industrial suppliers. By revealing true capacity, exposing real constraints, coordinating resources, and executing at the speed of operational demands, the front line gets what it needs to succeed. Job Description We are seeking an exceptional and experienced data engineer who shares our passion and obsession with quality. You'll be a core member of our product and engineering team dedicated to helping our clients replace time-consuming, manual processes to reach informed real-time decisions about government markets, competitors, and agency relationships. We need a skilled and dedicated data engineer to join our team to lead us in uncovering truth and meaning in data. You must be a hands-on engineer with a strong understanding of both data management and governance standards. You must also have strong interpersonal skills to work cross-functionally across internal teams as well as directly with end users and platform SMEs. This role is a full-time position located out of our office in Pittsburgh, PA. This role may require up to 10% travel. Scope of Responsibilities Define and lead the data lifecycle strategy across data acquisition, data ingestion, data cleansing, normalization and linkage. Ensure key entities within datasets are identified, resolved and linked to existing entities within the current master data repository. Apply various techniques to produce solutions to large-scale optimization problems, including data pre-processing, indexing, blocking, field and record comparison and classification. Improve data sharing, increase data repurposing and improve cost efficiency associated with data management efforts. Build best practices that help with chain of custody of data so it can be easily traced back to the source for accuracy and consistency. Work across functional teams to understand advanced statistical, machine learning, and text processing models and incorporate them into the existing data engineering infrastructure. Perform exploratory data analyses, generate and test working hypotheses, prepare and analyze historical data and identify patterns. Work directly with users as well as SMEs to establish, create and populate optimal data architectures and structures, as well as articulate techniques and results using non-technical language. Qualifications U.S. Citizenship is required. Bachelor's degree in Computer Science, Mathematics or a related technical field. Required Skills: 3-5 years experience with programmatically transforming data. Experience with RDBMS. Advanced SQL programming skills. Proficient usage of common data formats such as CSV, XML, and JSON. Strong analytical ability and attention to detail. Ability to work independently with little supervision. Desired Skills: Current possession of a U.S. security clearance, or the ability to obtain one with sponsorship. Experience using Amazon Web Services. Working knowledge with large (multiple terabytes) amounts of data. Air is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, sexual orientation, gender identity, disability and protected veterans status or any other characteristic protected by law.

🏢 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.