Senior Data Engineer (Snowflake)
Summary
Senior Data Engineer building production-grade Snowflake ELT/ETL pipelines and data products at a Kraków fintech. Day-to-day work centers on Snowflake data modelling, dbt/Airflow pipelines, SQL/Python automation, and AI-assisted development.
We are looking for an experienced Test Automation Engineer / Automation Quality Engineer to improve and modernise an existing test automation landscape. The role is focused on reducing test execution time, increasing release confidence, improving GUI regression coverage, and supporting the migration of test pipelines to GitHub Actions.
This is a hands-on role for someone who understands test automation at scale, can work close to backend/platform teams, and is comfortable using AI tools to identify gaps, troubleshoot issues, and speed up engineering work.
- Strong hands-on experience with Snowflake, including data modelling, performance tuning, cost optimisation, and security/governance features.
- Proven experience building production-grade ELT/ETL pipelines using tools such as dbt, Apache Airflow, Azure Data Factory, or similar.
- Expert-level SQL skills.
- Working knowledge of at least one general-purpose programming language, preferably Python, for automation and data processing.
- Practical experience using AI-assisted development tools such as Claude or Cursor to improve speed, quality, and documentation.
- Strong data quality mindset, including testing, monitoring, alerting, and observability.
- Ability to take ownership of well-scoped initiatives from unclear requirements to reliable, adopted data products.
- Strong communication skills and ability to explain technical data topics to business stakeholders.
Nice to have
- Experience with Power BI, including semantic models, DAX, Power Query, report design, row-level security, incremental refresh, and Power BI Service.
- Experience in finance, accounting, fintech, or another regulated environment.
- Familiarity with Microsoft Fabric, including data mirroring from Azure-hosted sources.
- Experience with cloud-native data architectures in Azure or AWS, such as Azure Synapse, Azure Data Lake, AWS Glue, or S3-based data lake patterns.
- Experience with streaming or event-driven ingestion using Kafka, Azure Event Hubs, or similar technologies.
- Understanding of Responsible AI principles and data infrastructure supporting auditable AI/ML pipelines.
- Interest in modern data stack trends, open-source tooling, or emerging analytics engineering practices.