Databricks Data Engineer

Nimble Approach is building a dedicated Databricks practice, and we’re looking for a hands-on data engineer to help build it with us. This is a chance to join a small, growing team early, work on real client platforms from day one, and develop your Databricks expertise alongside experienced consultants. You’ll design and build production-ready pipelines and lakehouse platforms on Databricks, helping clients replace fragmented data and tooling with something reliable, well governed and built to last. You’ll work as part of a delivery team, take ownership of meaningful pieces of work, and grow your technical and consulting skills as the practice grows. Your Impact 1. Delivering for Clients Build Production Pipelines: Design, build and maintain batch and streaming pipelines on Databricks using PySpark, SQL and Lakeflow Declarative Pipelines, with a focus on reliability, performance and data quality. Implement Lakehouse Platforms: Help stand up and extend client platforms, including medallion architectures, Delta Lake tables, Unity Catalog governance and workspace configuration. Engineer for Production: Apply CI/CD, automated testing and infrastructure as code (Databricks Asset Bundles, Terraform) so that what we deliver is repeatable, observable and easy to hand over. Work with Clients: Collaborate with client engineers and stakeholders to understand requirements, explain technical choices clearly, and raise risks or opportunities you spot along the way. 2. Growing the Practice Build Reusable Assets: Contribute to the accelerators, templates and delivery patterns that help us deliver Databricks projects faster and more consistently. Set the Standard: Follow, and help shape, our internal view of “what good looks like” for Databricks engineering: coding standards, review practices and documentation. Learn and Share: Develop your skills through pairing, code review and certification, and share what you learn with the wider team. What You Bring Hands-on Databricks Experience: You’ve built and supported production pipelines on Databricks, ideally including at least one end-to-end platform delivery. Strong PySpark and SQL: You write clean, tested, maintainable code and know how to diagnose and tune Spark performance. Lakehouse Fundamentals: You understand Delta Lake, medallion architecture, data modelling and ingestion patterns, and why data quality matters. Engineering Practices: You’re comfortable with Git, CI/CD and automated testing, and have some exposure to infrastructure as code. Cloud Experience: You’ve worked on at least one major cloud platform (Azure / AWS / GCP). Certification: Databricks certification is preferable Client-Ready Communication: You can explain technical concepts to non-technical people and are comfortable working directly with client teams. Nice to Have Unity Catalog implementation or migration experience Declarative Automation Bundles or Terraform for Databricks Streaming experience (Structured Streaming, Auto Loader, Kafka or Event Hubs) Migrating workloads from legacy platforms such as Synapse, Hadoop or on-premises warehouses Consulting or other client-facing delivery experience Exposure to Databricks AI/ML capabilities Why Nimble? We don't just want you to work here; we want you to grow here. We offer a flat structure where your voice shapes our OKRs and our future. Autonomy