Lead AWS Data Engineer

Job Summary • We are seeking an experienced Lead Data Engineer to support complex data engineering initiatives within our insurance data and analytics practice. • This role combines Deep technical expertise with strong coordination skills, working closely with onshore and offshore teams, business stakeholders, and project leadership to deliver enterprise data modernization and migration programs. • The candidate will serve as a technical point of contact for cross-functional teams while remaining hands-on with cloud data technologies. Key Responsibilities Technical Delivery • Design and implement end-to-end data pipelines using PySpark, Snowflake, and AWS cloud services. • Architect scalable ELT/ETL workflows and data warehouse models supporting insurance analytics use cases. • Drive data migration and modernization efforts from legacy environments to cloud-native platforms. • Develop and review complex SQL transformations, stored procedures, and data quality validation frameworks. • Establish and enforce data engineering standards, coding best practices, and pipeline documentation. • Provide hands-on troubleshooting and performance optimization across the data stack. Team Coordination & Stakeholder Engagement • Coordinate day-to-day activities across onshore and offshore data engineering teams to ensure timely delivery. • Serve as a technical point of contact for business stakeholders, translating requirements into engineering deliverables. • Facilitate requirement-gathering sessions, sprint planning, and status updates with project teams. • Communicate project progress, risks, and dependencies to project managers and client stakeholders. • Mentor junior engineers and conduct code reviews to uphold quality standards. • Collaborate with data architects, analysts, and QA teams throughout the project lifecycle. Required Skills & Qualifications Technical Skills • Deep experience with Snowflake including data modeling, performance tuning. • Proficiency with AWS services — S3, Glue, Lambda, EMR, Redshift, Step Functions, CloudWatch. • Strong experience building distributed data processing frameworks with Apache Spark / PySpark. • Advanced SQL skills — complex transformations, query optimization, and dimensional modeling. • Expertise in DWH design patterns — Kimball, Inmon, Data Vault, star and snowflake schemas. • Demonstrated experience leading or contributing to cloud migration and legacy modernization programs. • Familiarity with tools such as dbt, Apache Airflow, AWS Glue, or similar orchestration frameworks. • Solid Python programming for data engineering and automation tasks. Experience Requirements • 9 -12 years of progressive experience in data engineering. • Prior experience in insurance, financial services, or regulated industries preferred. • Experience coordinating distributed teams across time zones (onshore/offshore model). • Demonstrated ability to engage with non-technical stakeholders and translate business requirements. • Exposure to Agile/Scrum delivery methodology.