AVP, Lead Data Engineer

Lead Data Engineer

By joining Chubb as Lead Data Engineer for our North America Finance & Actuarial data platform, you'll set the engineering direction for a portfolio of strategic applications — Atlas, BAR, CDW, Posting, and Regulatory Reporting — that directly underpin financial, actuarial, claims, and regulatory decision-making across the enterprise. This is a technical leadership role: you'll define and drive engineering standards, architect and implement no-touch data pipelines and integrations, and transform the way our engineering squads deliver by embedding AI-assisted development, modern Dev Ops practices, and cloud-first design into everything we build.

You'll influence and guide a team of data engineers across multiple squads, provide hands-on technical leadership on the most complex initiatives, and serve as the senior engineering voice in cross-functional conversations with architects, platform teams, and business stakeholders.

Responsibilities Include:

• Define and drive the engineering strategy for the Finance & Actuarial data platform, establishing standards, patterns, and best practices across all squads.
• Architect and implement no-touch, automated data pipelines and integrations that minimize manual intervention, reduce operational risk, and improve reliability at scale.
• Lead the adoption of AI-assisted development practices — including AI-augmented code generation, pipeline automation, anomaly detection, and intelligent data quality monitoring — to accelerate delivery and reduce toil.
• Provide hands-on technical leadership on high-complexity initiatives, including cloud migration (Azure Synapse / Databricks / Snowflake), ETL modernization, and replatforming efforts.
• Evaluate and recommend modern tooling, frameworks, and architectural patterns; build the business case and lead adoption across engineering squads.
• Partner with the data reliability engineering and enhancement squad leads to ensure engineering standards are embedded in day-to-day delivery — from design through deployment and operations.
• Establish CI/CD pipelines, automated testing frameworks, and deployment standards that enable consistent, high-quality releases across Atlas, BAR, CDW, Posting, and Regulatory Reporting.
• Lead root cause analysis and resolution for the most complex data engineering issues, driving permanent fixes over tactical workarounds.
• Mentor and develop engineers across squads; build a culture of engineering excellence, continuous improvement, and accountability.
• Translate complex technical strategies into clear communications for executive stakeholders, including the Head of Data, North America, Head of Data Engineering, North America. and senior business leaders.
• Maintain and evolve technical documentation, architecture decision records, and engineering runbooks as living assets.
• Stay current on emerging data engineering technologies and bring relevant innovations into the team's delivery model.

• Bachelor's degree required in Computer Science, Computer Information Systems, Information Systems, Information Technology, Computer Engineering, or equivalent work experience.
• 12+ years of progressive data engineering experience, including hands-on ETL/ELT development, data warehouse design, and enterprise data pipeline delivery.
• 8+ years of experience with ETL development tools and concepts, with deep expertise in Informatica / IICS.
• 5+ years of experience with cloud data platforms including Azure (Synapse Analytics, Azure Data Factory, Azure Databricks) and Snowflake.
• Strong hands-on proficiency in Databricks (Delta Lake, Spark, notebooks, workflows) and Snowflake (data sharing, Snowpark, dynamic tables).
• 3+ years of experience with job scheduling tools such as Autosys or comparable distributed schedulers.
• 3+ years of scripting experience in Python, Shell, or comparable languages for pipeline automation and tooling.
• Demonstrated ability to architect and deliver no-touch, fully automated data pipelines in an enterprise environment.
• Proven track record of driving engineering transformation — adopting modern practices (CI/CD, infrastructure-as-code, automated testing, AI-assisted tooling) in a large, complex…