Lead Data Engineer

Job Description
We 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.

Base Compensation Range: 120,000 - 160,000

The posted range is the hiring range for this role — a subset of the broader range available to employees over time — and reflects base salary across our national hiring scale. Final offers are based on several factors, including the candidate's skills and experience, internal pay equity, work location, market conditions for the role, and the specific scope and responsibilities of the position. The top of the range is reserved for candidates who notably exceed the requirements; the lower end applies to those with less experience or fewer preferred qualifications. For positions based in higher-cost zones (e.g., California, New York, New Jersey), actual compensation may exceed the posted range; your recruiter will share specifics during the process.

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

Qualifications
Experience Requirements

6–9 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

Education

Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field