AVP, Data Engineering Manager

We are seeking a Data Engineering Manager to lead the development of scalable data pipelines, curated datasets, and innovative data products that drive our resilience and risk consulting services. In this role, you will be at the heart of building the data foundation that empowers analytics, benchmarking, and the creation of future digital products for our consulting teams and clients. As the ideal candidate, you are a hands-on leader who seamlessly navigates between technical detail and strategic priorities. You thrive in environments where data is fragmented, bringing structure and clarity to complex landscapes. Your commitment to data quality and governance builds trust across the organization, and your leadership inspires teams to deliver high-value outcomes. You are comfortable with ambiguity and motivated by the challenge of building something foundational. Your expertise enables you to unlock complex data environments independently, and your vision helps shape how data is governed and transformed into business value. This role is central to establishing the data backbone for our growing resilience and risk consulting organization. Your contributions will directly enhance our ability to deliver actionable insights, improve service efficiency, and develop future-ready data products. Join us and play a key role in shaping the future of data-driven consulting. Responsibilities Lead a team of data engineers responsible for ingesting, transforming, governing, and curating data from multiple internal and external sources Build and scale ETL/ELT pipelines and curated data layers on platforms such as Databricks, Snowflake, or similar modern cloud data environments Establish technical data quality standards and embed controls within pipelines and workflows Translate target schemes defined by clients and interpreted by senior analysts into trusted, business-ready tables and reusable data assets Own the ongoing maintenance, enhancement, and operational reliability of production data products Partner closely with analysts, consulting leaders, and product stakeholders to prioritize and deliver data capabilities that support consulting services Proven experience in data engineering, data platform engineering, or a related field, including hands-on experience with Python, ETL/ELT, and cloud-based data platforms comparable to technologies in complex enterprise environments Strong track record of centralizing and integrating data from diverse internal and external sources Experience curating datasets and tables based on a target schema for downstream analytics and business use cases Demonstrated success maintaining and evolving data products Prior leadership experience, with the ability to lead teams, raise performance, and deliver against ambitious goals Strong judgment, autonomy, and comfort operating in environments with layered security, governance, and access requirements Familiarity with enterprise data governance, lineage, metadata, and access-control practices Education and Experience Bachelor's degree in Computer Science, Geospatial Science, or Management information system, or related information management fields Minimum of 6 years of relevant experience Experience in insurance, reinsurance, financial services, or another highly governed industry Experience supporting analytics, consulting, benchmarking, or client-facing data products Experience building data capabilities that support both internal enablement services and externally facing product development Databricks Certified Data Engineer, SnowPro Core Certification, AWS Certified Data Engineer, or Certified Data Management Professional (preferred) The pay range for the role is $125,000 to $185,000. 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. Chubb offers a comprehensive benefits package, more details on which can be found on our careers website. The disclosed pay range estimate may be adjusted for the applicable geographic differential for the location in which the position is filled.