Join us on the R&D Software team as a Data Engineer (Databricks), and help shape the future of safer, more efficient, and more reliable operations across the globe. Start your journey with Anova today! Where you’ll work: This is a hybrid role based out of our Porto office. In practice, most of your work can be done remotely, with occasional in-office time in Porto for team collaboration — a flexibility our engineers consistently tell us they value. Job Duties and Responsibilities: You will build and run the Databricks pipelines that turn real-time telemetry and platform data into reliable, well-governed data assets — the master data that reporting, analytics and machine learning across Anova all depend on. Collaborate for success * Deliver Databricks ETL projects end to end, from requirements through to pipelines running in production. * Translate business goals into data solutions and help stakeholders make the right choices about data. * Contribute to technical decisions, take a significant share of the implementation, and monitor the pipelines you own once they are live. Build the One Anova data stream * Work with real-time telemetry from industrial IoT sensors deployed across the globe. * Build the BI aggregations that bring data from across platforms together into consistent, reusable data assets. * Your pipelines are the backbone for our internal natural-language digital assets that lets any employee query Anova's data without writing SQL. The reliability, freshness and clarity of what you publish directly determines whether that experience can be trusted. * Publish and maintain data assets as master data for the organization. Engineer with AI assistance * Use agentic coding tools — Claude Code, Copilot, Cursor and similar — as a normal part of daily delivery. * Hold AI-generated code to the same bar as any other code. You are accountable for what you ship. * Keep repositories, tests and documentation structured so both people and agents can work in them effectively. Advocate for quality * Contribute to and continuously adapt best practices and Ways of Working around data engineering, testing and pipeline operations. * Maintain clear data lineage and definitions for the assets you own — as AI agents increasingly query this data directly, untraceable or ambiguous data becomes a governance risk, not just a data-quality one. * Treat data quality as a feature: tests, expectations and monitoring, so problems surface before stakeholders find them. Minimum Requirements - * Bachelor's degree in Computer Science, Data Engineering, Data Science, or a related quantitative field or equivalent combination of education and experience * 5+ years of experience in data engineering or a closely related role, with hands-on production experience in Databricks (6–8 years preferred). * Significant experience building data workloads in Databricks, with a very good understanding of PySpark and Delta Lake. * Strong SQL — window functions, complex joins and query tuning are everyday tools for you. * Experience with streaming or incremental ingestion (Structured Streaming, Auto Loader, or equivalent) and the patterns that keep it correct: idempotency, checkpointing and schema evolution. * Data modelling for BI and analytics. * Good understanding of testing and CI/CD for Databricks workflows, alongside the software engineering and DevOps basics — git, code review, linters, unit tests and CI/CD pipelines are things you use daily. * Data quality practice: testing data as well as code, using pipeline expectations, dbt tests or similar. * Comfortable using agentic coding tools, with a clear view of where they help and where they need supervision. * Proficient in written and spoken English. Preferred Qualifications - * Databricks platform depth beyond the basics: Lakeflow pipelines (formerly Delta Live Tables), Lakeflow Jobs, Unity Catalog for governance and lineage, and infrastructure as code with Declarative Automation Bundles or Terraform. * Performance and cost optimization on Databricks: cluster sizing, Photon, liquid clustering, and partitioning. * The wider Azure data ecosystem: Event Hubs or Data Factory. * Master data management or data governance practice: clear ownership, stewardship and agreed definitions for shared data assets. * Domain experience in industrial, energy or IoT settings.

Senior Data Engineer (Databricks)

Similar jobs