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About the Role
We are seeking a Senior Databricks Data Engineer with proven hands-on Databricks experience to build, develop, and manage STACK’s enterprise data platform. This production-focused role requires expertise in Databricks, PySpark, Python, SQL, and Delta Lake. You will design reliable pipelines integrating various systems, establish high-quality datasets, and support scalable analytics and reporting. Collaborating with stakeholders, you will own data engineering solutions and enhance platform reliability and performance.
Responsibilities
- 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.
- 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.
- Optimize 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.
Requirements
- 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 optimization.
- 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.
- 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, prioritization, and problem-solving skills.
Skills
- Databricks
- PySpark
- Python
- SQL
- Delta Lake
- ETL/ELT
- APIs
- Databases
- SaaS applications
- Files
- Spreadsheets
- Lakehouse architecture
- Databricks Workflows
- Lakeflow
- Delta Live Tables
- Unity Catalog
- Git
- CI/CD
- Power BI
- Azure data services
Location
- Remote
Work Type
- Full-time
Experience Level
- Senior
Education Level
- Databricks Certified Data Engineer Professional
- Databricks Certified Platform Administrator