Lead Data Engineer
This role focuses on architecting and optimizing Databricks-based data pipelines while establishing performance and governance standards for an AI-native enterprise platform. The ideal candidate will drive operational excellence, mentor engineers, and shape scalable solutions for cost-efficient, high-performance data and AI workloads.
Skills required
About this role
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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