Build, migrate, stabilize, and optimize batch and streaming data pipelines on Databricks and AWS lakehouse platforms. Implement ingestion, transformation, testing, deployment, monitoring, security, governance, data quality, and metadata practices. Support data onboarding, mastering and golden-record integration, reusable engineering patterns, CI/CD, infrastructure automation, and operational readiness. Provide technical guidance to global teams and use AI-assisted engineering tools responsibly for coding, documentation, testing, and troubleshooting.

Develop and deploy Java applications using Spring Boot, microservices architecture, cloud platforms, and containerization technologies. Optimize SQL and PL/SQL queries, maintain application performance and quality, and support code automation and organization. The role requires collaboration on business-critical index analytics solutions and participation in modern enterprise application development.

Leads an offshore data engineering team responsible for migrating and optimizing pipelines on Databricks and AWS. Oversees cloud-native ingestion, transformation, governance, security, observability, CI/CD, and production support. Supports enterprise data onboarding and integration with mastering platforms such as NeoXam DataHub, including data quality, metadata, reconciliation, and golden-record workflows. Partners with global stakeholders, mentors engineers, and drives reliable, standardized engineering practices across batch and streaming data environments.