Lead Data Engineer (Databricks)

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About the project

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Your responsibilities

• Build, evolve, and operate data ingestion and processing capabilities for structured, semi-structured, and unstructured data, supporting the transition from early prototypes through early adoption and general release.
• Implement and maintain rich metadata and data quality practices that enable cross- record querying, traceability, and AI-ready data access across experiments, files, inventory, and workflows.
• Work with architects, AI engineers, workflow engineers, and domain experts to ensure data is usable, performant, and trustworthy for downstream GenAI and analytics use cases.
• Support engineering quality through code reviews and mentoring of junior/mid-level engineers.
• Lead architectural decisions and work with senior business and technology stakeholders.Guide and mentor data and engineering teams

Our requirements

• Experience as a Lead / Principal Data Architect in multinational environments.
• Deep knowledge of data modelling, integration, warehousing, and modern data platforms.
• Strong hands-on Python and complex SQL, with production Spark experience on Databricks (Structured Streaming, Auto Loader, Delta Lake, Unity Catalog) to support analytics, AI/ML, and enterprise application use cases.
• Practice working with structured and unstructured data across the full lifecycle: ingestion, transformation, enrichment, indexing, and retention, delivered through disciplined engineering practice including infrastructure-as-code, CI/CD, and automated testing of pipelines.
• Proven experience ingesting from heterogeneous operational sources including relational (Oracle, PostgreSQL) and document stores (MongoDB), using Full load + CDC or equivalent replication patterns, with practical handling of schema drift, deletes, and late or out-of-order data.
• Skills in data modelling across a layered architecture, with clear contracts between raw, curated and serving layers, supporting cross-entity queries and contextual linking, and performant data access patterns.

Optional

• Experience building unstructured document pipelines: parsing and extraction from PDF, Office and scanned formats, chunking, embedding generation, and maintaining searchable indexes at scale.

What we offer

• Private medical care
• Multisport card
• Friendly, informal atmosphere, and direct contact with everyone in the company
• Modern technologies

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