Lead the architecture, development, and maintenance of enterprise data platforms supporting transactional, analytical, batch, and streaming workloads. Design scalable data models, pipelines, schemas, and distributed systems; optimize SQL and NoSQL performance; establish architecture standards, governance, security, observability, and reliability practices; and support reporting and analytics. Collaborate with technical and business stakeholders, guide architecture decisions, and mentor engineering teams.

Build and optimize scalable batch and streaming ETL/ELT pipelines using Databricks, Apache Spark, Python, and PySpark. Develop data models for operational and analytical workloads, implement security and governance controls, monitor data quality and performance, and improve distributed data processing solutions. Collaborate with product and engineering teams, maintain code quality, write pipeline tests, and evaluate emerging data technologies.