Contract Databricks Data Engineer
Location: London (Hybrid - 2 days per week onsite)Contract Length: Initial 6 months Day Rate: Flexible (Inside & Outside IR35 considered - final determination pending)
We're partnering with a client seeking a skilled Databricks Data Engineer to support the build and evolution of a modern data platform. This is a hands-on contract role, ideally suited to someone who thrives working across both new platform development and ongoing production support within a Databricks-driven environment.
The RoleThis role sits at the heart of a Databricks Lakehouse implementation, where you'll be responsible for building, optimising, and maintaining scalable data pipelines and datasets to support analytics and business intelligence.
You'll work closely with technical and business stakeholders to deliver high-quality, performant data solutions while also ensuring the stability and reliability of existing workloads.
Key Responsibilities- Design, build, and optimise data pipelines within Azure Databricks using PySpark and Spark SQL
- Develop and manage Delta Lake-based data models, supporting a structured Lakehouse / Medallion architecture (Bronze, Silver, Gold layers)
- Support the ingestion and transformation of large-scale data from multiple sources into Databricks
- Contribute to the modernisation and migration of legacy SQL Server workloads into Databricks
- Monitor, troubleshoot, and improve the performance of existing data pipelines and jobs
- Work closely with stakeholders to ensure data is reliable, well-structured, and ready for analytics and reporting
- Integrate and orchestrate workflows using tools such as Azure Data Factory or Databricks Workflows
- Collaborate with BI teams to ensure datasets are optimised for Power BI and downstream consumption
- Strong commercial experience working with Azure Databricks as a core data processing platform
- Deep expertise in:
- PySpark / Apache Spark for distributed data processing
- Delta Lake (ACID transactions, optimisation, data versioning)
- Spark SQL and advanced SQL techniques
- Python for data engineering and pipeline development
- Hands-on experience designing and implementing Lakehouse architectures
- Experience migrating data platforms from on-premises systems into cloud-based Databricks environments
- Solid understanding of data modelling, ETL/ELT design, and performance optimisation
- Familiarity with orchestration tools such as Azure Data Factory, Airflow, or Databricks Jobs
- Experience working in production environments with both project delivery and BAU responsibilities
- Exposure to Unity Catalog, data governance, and fine-grained access control in Databricks
- Experience implementing CI/CD pipelines for Databricks (e.g. via Azure DevOps or Git integration)
- Knowledge of streaming data pipelines (Spark Structured Streaming / Kafka)
- Experience working with cloud-native data architectures in Azure
- Prior exposure to Power BI or similar BI/reporting tools