Databricks Data Engineer

We   are   hiring   a   Databricks   Data   Engineer   to design,   build , and   operate   scalable   data   pipelines   and   curated   data   products   on   Databricks .   You   will   work   across   ingestion ,   transformation ,   governance , and   delivery   layers — using   SQL,   Python , and   PySpark - while   applying   strong   data   warehousing   principles   ( Kimball ). This   role   requires   hands -on   experience   with   Databricks   platform   capabilities ,   including   Unity   Catalog   and   Lakeflow   Declarative   Pipelines , and a   disciplined   approach   to   quality   using   validation / expectations .   Key   Responsibilities   Build   and   maintain   production-grade   data   pipelines   in   Databricks   using   SQL,   Python , and   PySpark .   Implement   ELT/ETL   patterns   for   batch   and ( where   relevant )   streaming   data   processing .   Develop   and   maintain   Lakehouse   data   models   and   curated   datasets   aligned   with   DWH   best   practices   ( Kimball / Inmon / Data   Vault ).   Use   Databricks-native   capabilities   to   implement   robust ,   maintainable   pipelines   ( e.g .,   Lakeflow   Declarative   Pipelines ).   Implement   data   quality   checks   ( e.g .,   Expectations ) and monitoring to   ensure   reliability   and trust in   data   products .   Configure   and   manage   governance   and   access   controls   using   Unity   Catalog ,   including   catalogs / schemas ,   permissions , and   lineage-friendly   practices .   Optimize   performance   and   cost   ( cluster   sizing ,   partitioning ,   file   sizes ,   caching ,   query   optimization ).   Collaborate   with   analytics ,   data   science , and   engineering   stakeholders   to   translate   requirements   into   well-defined   data   contracts   and   deliverables .   Create   and   maintain   technical   documentation   for   pipelines ,   models , and   operational   runbooks .   Support   operational   excellence: incident   response ,   root-cause   analysis , and   continuous   improvement   of   data   platform   reliability .   Required   Qualifications   Proven ,   hands -on   Databricks   experience   in   production   environments .   Strong   working   knowledge   of SQL,   Python , and   PySpark   for   data   engineering   workloads .   Practical   experience   with   Databricks-specific   technologies   such   as:   Lakeflow   Declarative   Pipelines   (DLT)   Expectations   /   data   quality   validation   patterns   Unity   Catalog   ( governance ,   access   control ,   catalog / schema   management)   Other   Databricks   platform   components   relevant   to   pipeline   development   and   operations   Solid   experience   with   data   warehousing   design and modeling   methodologies   ( Kimball ,   Inmon , or   Data   Vault ).   Understanding   of   data   engineering   fundamentals :   orchestration   patterns ,   incremental   processing ,   SCDs ,   metadata   management, and   observability .   Nice -to- Have   Qualifications   Experience   with   Microsoft SQL Server and T-SQL.   Experience   working   in   Azure   ( e.g ., ADLS,   Azure   networking / security   concepts , identity/ auth ).   Proficiency   with   Git- based   workflows   ( branching ,   code   reviews ) and CI/CD   for   data   pipelines .   Working   Style   and   Collaboration   Ownership   mindset :   you   build   it ,   you   run   it .   Pragmatic   engineering :   focus   on   reliability ,   clarity , and   maintainability   over “ clever .”   Strong   communication :   ability   to   align   stakeholders   on   definitions ,   assumptions , and   trade-offs . The starting salary for this position is €5,500 per month (B2B type of cooperation). We offer: A Truly Global Workplace – work with professionals from 40+ nationalities, bringing diverse expertise, perspectives, and a collaborative international culture. Hybrid & Flexible Work – we support work-life balance with remote work options and modern office spaces across Europe. A Culture of Growth – we invest in your future, offering LinkedIn Learning, mentorship, and professional development programmes, including HiPo and leadership development initiatives to support career advancement. Financial Growth Opportunities – benefit from our share purchase matching programme, allowing you to invest in your future with matched contributions and long-term financial rewards. Workation Programme – work remotely from different countries for up to 2 months per year, experiencing new cultures while staying connected and productive. We may use artificial intelligence (AI) tools to support specific parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses against predefined criteria. These tools assist our recruitment team but do not replace human judgment. All final hiring decisions are made by human recruiters. By proceeding to apply for a job with us, you confirm that you have read and accepted our Recruitment Privacy Policy