Senior Data Engineer

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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