Databricks Data Engineer

We are looking for a Senior Databricks Data Engineer with proven hands-on Databricks experience to build, develop and manage STACK’s enterprise data platform. This is a production-focused engineering role requiring practical expertise in Databricks, PySpark, Python, SQL and Delta Lake.

The role will design reliable pipelines integrating Finance, Delivery, Operations, HR and PMO systems, establish governed and high-quality datasets, and support secure, scalable analytics and reporting. Working with technical and business stakeholders, the successful candidate will take ownership of data engineering solutions and help improve the reliability and performance of the platform.

• Design, develop, deploy and maintain production-grade ETL/ELT pipelines using Databricks, PySpark, Python and SQL, integrating APIs, databases, SaaS applications, files and spreadsheets.
• Develop and optimize scalable Delta Lake data models and lakehouse architecture for reporting, analytics and future AI use cases.
• Build, schedule, monitor and troubleshoot data processing jobs using Databricks Workflows, Lakeflow or Delta Live Tables, as appropriate.
• Apply Unity Catalog governance, including access permissions, data ownership, lineage and common data definitions.
• Support Databricks workspaces, compute resources, SQL Warehouses, service principals and access management in partnership with relevant technology teams.
• Manage development, test and production deployments, monitoring, incident resolution and recovery, using Git and CI/CD practices.
• Optimise pipeline performance, Spark processing, compute usage and platform costs while maintaining appropriate security and operational controls.
• Implement data quality checks for completeness, accuracy, timeliness, consistency, duplicates and reconciliation with source systems.
• Build monitoring and alerting to identify pipeline failures, data quality issues and operational risks.
• Integrate reliable, governed Databricks datasets with Power BI and other approved analytical tools.
• Work with Finance, Delivery, Operations, HR and PMO teams to translate requirements into sustainable data solutions.
• Document pipelines, dependencies, controls and operating procedures, and coordinate specialist external partners when required.

• Strong hands-on experience building and supporting production data pipelines directly in Databricks; experience with other cloud data platforms alone is not sufficient.
• Practical experience with PySpark, Python, SQL and Delta Lake, including data transformation and performance optimisation.
• Experience orchestrating and troubleshooting Databricks data pipelines using Workflows, Delta Live Tables or Lakeflow.
• Working knowledge of Unity Catalog, data governance, access controls and data quality practices (Essential).
• Experience integrating data from APIs, business applications, databases and files.
• Experience supporting Databricks workspaces, compute resources or SQL Warehouses in a production environment.
• Experience with Azure data services, Power BI, Git and CI/CD practices.
• Understanding of environment management, monitoring, troubleshooting, security and cost control.
• Ability to work with non-technical stakeholders, explain data issues clearly and document solutions effectively.
• Strong ownership, prioritisation and problem-solving skills.
• Databricks Certified Data Engineer Professional or Databricks Certified Platform Administrator (if available).