Lead Data Architect

Our passion at EXL is enabling organizations to achieve a measurable, competitive advantage through Data Management. We partner with our clients to design and implement scalable, accessible, and innovative solutions. Our goal is to make sense of data to drive client’s business forward through data strategy, data governance and building cloud data platforms. EXL is a leading data analytics and digital operations and solutions company that partners with clients to improve business outcomes and unlock growth.

By bringing together deep domain expertise with robust data, powerful analytics, cloud, artificial intelligence (“AI”) and machine learning (“ML”), we create agile, scalable solutions and execute complex operations for the world’s leading corporations in industries including insurance, healthcare, banking and financial services, media, and retail, among others. Focused on driving faster decision‑making and transforming operating models, EXL was founded on the core values of innovation, collaboration, excellence, integrity and respect.

Headquarters in New York, our team is over 44,500+ strong, with more than 50 offices spanning six continents.

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.

• 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

• 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

• Deep experience with Snowflake including data modeling, performance tuning
• Proficiency with AWS services — S3, Glue, Lambda, EMR, Redshift, Step Functions, Cloud Watch
• Strong experience building distributed data processing frameworks with Apache Spark / Py Spark
• 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

• 10+ 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

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