Data Engineering Tech Lead

· Strong experience designing, implementing, and governing modern cloud-native data platforms using AWS, AWS Glue, Snowflake, and related cloud data services.

in large-scale data modernization and migration initiatives is preferred.

· Proven expertise in Data Warehousing, Data Modeling, ETL/ELT architecture, data integration, data quality, metadata management, and scalable data pipeline design for enterprise analytics and operational workloads

· Hands-on experience developing and optimizing data solutions using Python, PySpark, Informatica, DataStage, AWS Glue, Snowflake, and related cloud-native data engineering technologies.

Ability to guide engineering teams on architecture, coding standards, and implementation best practices.

· Demonstrated leadership in architecting and delivering solutions on modern data platforms, including Data Lakes, Lakehouses, Cloud Data Warehouses, and real-time data processing ecosystems.

Hands-on implementation experience is required.

· Strong understanding of Generative AI, Agentic AI, and AI-enabled engineering concepts, including practical application of GenAI across the Software Development Lifecycle (SDLC), data engineering workflows, reverse engineering, migration acceleration, code generation, testing, documentation, and productivity optimization.

· Familiarity with Insurance industry platforms and core systems, including Policy Administration, Claims, Billing, Underwriting, Data & Analytics ecosystems, and digital modernization initiatives.

Knowledge of platforms such as Guidewire, Duck Creek, Majesco, or equivalent insurance systems is desirable.

· Strong consulting, solutioning, and stakeholder management skills with the ability to work closely with data, cloud, architecture and business to define transformation roadmaps and deliver business value through data-driven solutions.