VP, Principal Data Engineer

About the role

By joining Chubb as Principal Data Engineer for our North America Finance & Actuarial and Corporate Data platforms, you'll set the engineering direction for a portfolio of strategic applications that directly underpin financial, actuarial, claims, regulatory, underwriting and executive 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 DevOps 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

• Define and drive the engineering strategy across Finance, Actuarial, and Corporate Data platforms, establishing and enforcing 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 a variety of different data systems and products.
• Lead root cause analysis and resolution for the most complex data engineering issues, driving permanent fixes over tactical workarounds.
• Build, lead, and develop a team of approximately five direct-report senior data engineers and engineering leads across Finance, Actuarial, and Corporate Data squads; drive performance, engineering accountability, and a clear path for career growth.
• Define, govern, and enforce engineering standards and best practices across distributed and offshore engineering teams; hold ultimate accountability for the quality, consistency, and technical direction of engineering products delivered by those teams, regardless of reporting structure.
• 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.

Qualifications

• 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, Databricks or other similar technologies.
• 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 data organization.
• Strong understanding of data warehousing concepts including dimensional modeling, data lineage, data quality frameworks, and enterprise DW architecture.
• People leadership required: must have managed direct-report senior data engineers and/or technical leads. Demonstrated experience governing distributed and offshore engineering teams required; this role carries ultimate engineering accountability for products delivered by those teams, including standards adherence, quality, and technical direction.
• Excellent communication skills; able to translate engineering complexity into clear, credible language for both technical teams and executive stakeholders.
• Insurance industry experience preferred; P&C domain knowledge (financial reporting, actuarial data, claims, regulatory/bureau reporting) a strong plus.
• Location: must be based in Whitehouse Station or Jersey City, NJ, Philadelphia, PA, or Simsbury, CT.

Pay

The pay range for the role is $183,700 to $240,000. The specific offer will depend on an applicant’s skills and other factors. This role may also be eligible to participate in a discretionary annual incentive program. The disclosed pay range estimate may be adjusted for the applicable geographic differential for the location in which the position is filled.