Data Engineer

As a dynamic multinational tech company operating in 50 countries, we drive innovation and create projects that shape the future and greatly enhance the quality of life. You will find our solutions in the space industry, supporting scientists in the development of cancer drugs, and implementing innovative technological solutions for industrial clients worldwide. These are just some of the areas in which we operate! Data Engineer Location : Remote from Poland Employment Type : B2B or Employment Contract FTE : Full-time The Data Engineer will join the “experience” scrum team and collaborate with a Data Scientist, supporting the process from data preparation for the ML model through to reviewing and deploying the Data Scientist’s code to production in line with defined standards. Requirements Strong coding experience in Python (or similar languages) with a Test-Driven Development (TDD) approach. Solid SQL skills with experience in data warehousing Proven experience with GIT as a code versioning tool AI Coding Assistance experience Hands‑on experience with AWS services: Athena, Lambda, Aurora, EventBridge, Glue, SageMaker, S3, MWAA. Experience working with Data Scientist and providing them with Data and Infrastructure support. Ability to understand business context and requirements to translate them into actionable technical actions Ability to relate data to business processes Clear, concise communication with engineers, product, and data stakeholders. Pragmatic mindset: balances engineering rigor with delivery. Ownership: pushes features from design through production with measurable outcomes. Responsibilities Develop and maintain data pipelines by modifying and extending existing AWS Lambda functions and SageMaker processing jobs, integrating external data sources, and refactoring ingestion and transformation logic to support new features and datasets, while implementing and maintaining robust data quality checks within pipelines. Design and optimize data models for Silver and Gold layers using Apache Iceberg format, ensuring scalable schema design, proper data partitioning, and adherence to modeling best practices, while improving SQL query performance and monitoring execution efficiency. Ensure data quality and governance by defining and enforcing validation rules, building automated data quality frameworks, monitoring data consistency across datasets, and generating regular quality reports for stakeholders. Document data processing workflows, transformations, and architectural decisions, and maintain all code and datasets in GitLab with proper version control, meaningful commit history, and clear collaboration practices. #J-18808-Ljbffr