Lead Data Engineer

We are seeking an experienced Senior Data Engineer to drive the performance governance and AI-native maturity of our enterprise Data Platform in Databricks. This is a Databricks-focused Data Engineering role with a working understanding of DevOps practices designing scalable pipelines tuning workloads for performance and cost and operationalizing modern data and AI capabilities on Lakehouse.

The ideal candidate has deep hands-on Databricks expertise a strong performance-engineering instinct and a builders mindset for AI-assisted operations. Youll own the Databricks performance and governance standards for the platform mentor engineers and shape the direction for AI-native operations.

• Design and develop scalable data pipelines and Lakehouse solutions on Databricks.
• Tune Databricks workloads for performance and cost including cluster sizing query optimization and Delta Lake table design.
• Establish and enforce 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.
• Drive CI/CD workflows for Databricks assets setting DevOps best practices for deployment and release management.
• Lead design reviews and mentor Data Engineers on Databricks best practices and AI-native features.
• Own Databricks vendor coordination case management escalations and release adoption strategy.

• Within 612 months youll define the platforms tuning and governance standards lead design reviews mentor junior engineers and shape the AI-native operations roadmap.

• Bachelors or Masters degree in Computer Science Information Technology or equivalent relevant experience.
• 6 years of data engineering experience with 2 years hands-on Databricks in enterprise settings.
• Deep understanding of Databricks Lakehouse architecture Delta Lake Unity Catalog and Workflow orchestration.
• Proven ability to tune Spark workloads for cost and performance at production scale.
• Advanced Python (PySpark) and SQL skills.
• Working knowledge of CI/CD practices and DevOps principles applied to data workloads.
• Experience with observability tooling for Databricks.

• Experience with Databricks-native AI capabilities and agentic frameworks.
• Familiarity with Databricks Serverless Compute and DBSQL performance tuning.
• A Databricks Certified Professional.
• Exposure to Infrastructure-as-Code is a plus.

• 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.
• Influence outcomes across source teams vendors and business stakeholders without direct authority.

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