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. More information about NXP in India... #LI-7013