Lead AWS Data Engineer

Senior Data EngineerWe are seeking an experienced Senior 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 ResponsibilitiesDesign and implement end-to-end data pipelines using PySpark, Snowflake, and AWS cloud servicesArchitect scalable ELT/ETL workflows and data warehouse models supporting insurance analytics use casesDrive data migration and modernization efforts from legacy environments to cloud-native platformsDevelop and review complex SQL transformations, stored procedures, and data quality validation frameworksEstablish and enforce data engineering standards, coding best practices, and pipeline documentationProvide hands-on troubleshooting and performance optimization across the data stackTeam Coordination & Stakeholder EngagementCoordinate day-to-day activities across onshore and offshore data engineering teams to ensure timely deliveryServe as a technical point of contact for business stakeholders, translating requirements into engineering deliverablesFacilitate requirement-gathering sessions, sprint planning, and status updates with project teamsCommunicate project progress, risks, and dependencies to project managers and client stakeholdersMentor junior engineers and conduct code reviews to uphold quality standardsCollaborate with data architects, analysts, and QA teams throughout the project lifecycleRequired Skills & QualificationsTechnical SkillsDeep experience with Snowflake including data modeling, performance tuningProficiency with AWS services — S3, Glue, Lambda, EMR, Redshift, Step Functions, CloudWatchStrong experience building distributed data processing frameworks with Apache Spark / PySparkAdvanced SQL skills — complex transformations, query optimization, and dimensional modelingExpertise in DWH design patterns — Kimball, Inmon, Data Vault, star and snowflake schemasDemonstrated experience leading or contributing to cloud migration and legacy modernization programsFamiliarity with tools such as dbt, Apache Airflow, AWS Glue, or similar orchestration frameworksSolid Python programming for data engineering and automation tasksExperience Requirements6–9 years of progressive experience in data engineeringPrior experience in insurance, financial services, or regulated industries preferredExperience coordinating distributed teams across time zones (onshore/offshore model)Demonstrated ability to engage with non-technical stakeholders and translate business requirementsExposure to Agile/Scrum delivery methodologyEducationBachelor's degree in Computer Science, Information Systems, Engineering, or a related field