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.