About this role
Position Summary: We are seeking a hands-on Data Engineer building on Databricks who is growing their Lakehouse and performance-engineering depth, with a builder's mindset for AI-assisted operations.
Key Responsibilities:
• Design and develop scalable data pipelines and Lakehouse solutions on Databricks. • Build and tune Databricks workloads for performance and cost, including cluster sizing, query optimization, and Delta Lake table design. • Implement and utilize best practices for partitioning, clustering, and workload isolation. • Track performance trends, identify high-cost queries, and partner with source teams and end users to resolve long-running loads. • Design and operationalize Unity Catalog for data governance — access control, lineage, and security. • Build monitoring and self-healing automation using Databricks-native AI and agentic capabilities. • Contribute to CI/CD workflows for Databricks assets, applying DevOps best practices for deployment and release management. • Deliver assigned pipelines and workloads with guidance from senior engineers, growing toward independent ownership.
What Success Looks Like (First 6–12 Months)
• In your first 6–12 months, you'll independently build and tune production pipelines, implement Unity Catalog access controls as designed, and contribute to monitoring automation.
Required Qualifications:
• Bachelor or Master’s degree in Computer Science, Information Technology or equivalent years of relevant experience. • 3+ years of data engineering experience (with a focus on data integration), including 1+ years hands-on Databricks in enterprise settings. • Solid understanding of Databricks Lakehouse architecture, Delta Lake, Unity Catalog, and Workflow orchestration. • Working ability to tune Spark workloads for cost and performance. • Strong Python (PySpark) and SQL skills. • Working knowledge of CI/CD practices and DevOps principles applied to data workloads. • Experience with observability tooling for Databricks.
Preferred Qualifications:
• Experience with Databricks-native AI capabilities and agentic frameworks. • Familiarity with Databricks Serverless Compute and DBSQL performance tuning. • A Databricks Certified Professional is nice to have. • Exposure to Infrastructure-as-Code is a plus.
Competencies:
• Performance-engineering mindset — measures, tunes, and re-measures. • Curiosity for AI-native operations and continuous automation. • Strong sense of platform ownership — quality, cost, and reliability. • Effective communication with engineering peers, vendors, and business stakeholders.
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