Lead Data Engineer

Position Summary

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 builder's mindset for AI-assisted operations. You'll own the Databricks performance and governance standards for the platform, mentor engineers, and shape the direction for AI-native operations.

Key Responsibilities

• 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.
  
  

What Success Looks Like (First 6–12 Months)

• Within 6–12 months, you'll define the platform's tuning and governance standards, lead design reviews, mentor junior engineers, and shape the AI-native operations roadmap.
  
  

Required Qualifications

• Bachelor's or Master's 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.
  
  

Preferred Qualifications

• 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.
  
  

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

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