AVP, Lead Data Engineer

Chubb•Philadelphia, PA

2d•Onsite

About The Position

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 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.

Requirements

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 data organization.
Strong understanding of data warehousing concepts including dimensional modeling, data lineage, data quality frameworks, and enterprise DW architecture.
Experience leading or influencing cross-functional engineering teams; prior people management experience a plus.
Excellent communication skills — able to translate engineering complexity into clear, credible language for both technical teams and executive stakeholders.

Nice To Haves

Insurance industry experience preferred; P&C domain knowledge (financial reporting, actuarial data, claims, regulatory/bureau reporting) a strong plus.

Responsibilities

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.