Senior Data Engineer

• Prestigious position at one of the world’s largest banks
• Stable, long-term projects
• Competitive salary with a B2B contract
• Hybrid work (6 days per month from the office in Cracow)
• Private healthcare and multisport card
• Personal growth and development opportunities with the possibility to rotate between projects

Daily tasks

• Design, build and run end-to-end pipelines covering ingestion, extraction, transformation, enrichment, validation and publication
• Develop reusable connectors and processing components for structured and unstructured sources (APIs and other interfaces)
• Build reliable pipelines with idempotent writes, deduplication, retries, replay, backfills and recovery from partial failures
• Design incremental and full-load patterns suited to each source’s volume and change behaviour
• Develop SQL data models and transformations, with a focus on integrity and query performance
• Design graph schemas and translate ontology concepts into implementable models, mapping source data to the knowledge graph
• Build graph loading and query patterns that preserve identifiers, relationships, provenance and lineage
• Design NoSQL models around access patterns, and integrate relational, graph and NoSQL stores
• Implement data quality controls, lineage tracking and operational monitoring (throughput, freshness, failure rates)
• Help set standards for security, access control, observability and data lifecycle across pipelines
• Contribute to automated testing, schema migrations, CI/CD and production support
• Work with analysts, architects, engineers and business stakeholders to turn source requirements into technical designs

Requirements

– Proven experience as a Senior Data Engineer delivering production-grade data pipelines – Strong SQL skills: complex transformations, relational modelling, query-plan analysis and optimisation – Hands-on experience with graph databases and knowledge graphs, including ontology and entity/relationship modelling – Practical experience designing NoSQL data models (partitioning, indexing, consistency trade-offs) – Strong Python skills (or equivalent) for orchestration, transformation and testing – Experience building reusable pipeline frameworks across multiple sources or domains – Good understanding of batch and event-driven processing, incremental loads, idempotency and backfill strategies – Experience with structured and unstructured data, including document content and metadata – Solid knowledge of data quality, schema evolution, lineage and data lifecycle management – Experience with cloud data services, orchestration tools, CI/CD and monitoring – Clear communication and experience working across engineering, architecture and business teams

Must have: Python, SQL, NoSQL, CI/CD