Lead Data Engineer - Data Engineering 4C

Primary Focus: End-to-end Data Engineering, Data Modeling, Delta Lake \& Lakebase Optimizations, Secure Lakehouse Management, Production Migrations, and Complex Pipeline Debugging. Role Overview We are seeking an experienced Senior Data Engineer (Band 4C) to drive the design, development, optimization, and production deployment of our Finance1 Databrics platform. In this role, you will lead end-to-end data pipeline engineering, design scalable dimensional and lakehouse data models, manage end-to-end production migrations, and troubleshoot complex production issues. You will be responsible for query performance tuning, maintaining Delta table optimizations, enforcing strict data governance via Unity Catalog, and ensuring zero-downtime, high-availability data delivery across environments. Key Responsibilities

• Data Modeling: Design, build, and maintain enterprise-grade data models (Dimensional/Star Schema) alongside comprehensive Source-to-Target Mappings (STTM) and Seed Data Management.
• Core Databricks \& Lakehouse Pipeline Engineering: Build robust batch ingestion/processing pipelines using PySpark, Delta Lake, Lakebase, and Databricks Workflows.
• Production Migration \& Environment Management: Lead end-to-end production migrations, environment promotions (Dev/QA/Prod), historical data backfills, schema migrations, and legacy system transitions onto the Databricks Lakehouse platform with minimal downtime.
• Production Debugging \& Operational Support: Troubleshoot and resolve complex production pipeline failures, memory leaks, Spark driver/worker OOM (Out Of Memory) issues, data skewness, schema drift, and performance bottlenecks.
• Performance Tuning \& Optimization: Perform execution plan analysis, query tuning, and compute optimization across Serverless/Classic SQL Warehouses, Delta table layout optimizations (Z-Ordering, Liquid Clustering, Compaction), and caching strategies.
• Governance \& Security: Implement and enforce enterprise-grade data security, column/row-level access controls, data masking, and audit lineage using Databricks Unity Catalog.
• CI/CD \& Lakehouse Automation: Implement automated deployment pipelines for Databricks assets using Git, Databricks Asset Bundles (DABs), and DevOps practices.

Required Qualifications \& Technical Skills

• Experience: 6 to 10 years of core Data Engineering experience with strong, hands-on expertise on the Databricks Lakehouse platform.
• Production \& Debugging Expertise: Proven track record of managing production data pipelines, debugging complex distributed systems, analyzing Spark UI/DAG logs, and resolving operational outages.
• Migration Experience: Hands-on experience executing complex production migrations, data cutovers, schema evolution handling, and historical data loads.
• Core Technical Stack: PySpark, SQL, Delta Lake internals, Databricks SQL Warehouses, Lakebase, and Unity Catalog.
• Data Modeling \& STTM: Expertise in conceptual, logical, and physical data modeling alongside detailed STTM creation.
• Governance \& Security: Hands-on experience configuring Unity Catalog, Access Control Lists (ACLs), role-based access control (RBAC), and data privacy policies.
• DevOps \& Orchestration: Practical knowledge of Databricks Asset Bundles (DABs), Git workflows, and CI/CD pipelines.
• Exposure to any other data platform like Snowflake, Microsoft Fabric

Qualifications

Bachelors - Business Analytics, Bachelors - Computer Science, Bachelors - Statistics, Masters - Data Science