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YOUR ROLE

  • Design, build and own scalable data pipelines on Azure using Databricks, PySpark and Spark SQL, from requirements to productive operation.
  • Implement Delta Lake solutions, including ingestion, transformation and Medallion architecture (Bronze/Silver/Gold).
  • Define and implement data governance, access control and lineage with Unity Catalog.
  • Tune Spark jobs, clusters and storage for performance and cost (partitioning, caching, cluster sizing).
  • Ensure data quality through validation, testing and monitoring, and resolve data issues with root-cause analysis.
  • Integrate data from databases, APIs, files and streaming platforms.
  • Work with data analysts, data scientists and architects, and prepare data for business users, including dashboards on Databricks Apps.
  • Keep code in Git and follow CI/CD and documentation best practices; share your working style through reviews and pairing.

YOUR PROFILE

  • 6–8 years of data engineering experience, including 3–4 years of hands-on Databricks.
  • Strong Python, PySpark and Spark SQL.
  • Experience with Azure services: ADLS Gen2, Data Factory, Key Vault, Event Hubs.
  • Strong command of Delta Lake, Unity Catalog, Lakeflow Jobs (formerly Databricks Workflows) and Lakeflow Declarative Pipelines (formerly Delta Live Tables).
  • Good understanding of data modelling and data warehousing, and proven experience designing a data platform on Azure.
  • Strong communication skills and the ability to work with international clients (good English).
  • Bachelor's or Master's degree in Computer Science, IT or a related field.
  • Databricks Certified Data Engineer Professional
  • Exposure to data science and ML workflows

Apply now »