Data Engineer

Chubb's PRS Data Science team builds data pipelines and analytical solutions that power predictive models directly supporting our insurance products and business partners. We operate in a fast-paced, collaborative environment where engineers own work end-to-end—from ingestion and transformation to delivery of production-ready datasets consumed by data scientists and decision-makers across the organization.

We are looking for an early to mid-career Data Engineer who is passionate about building reliable, scalable pipelines and who thrives at the intersection of engineering and business impact.

Responsibilities

- Design, build, and maintain production ETL pipelines for predictive modeling workloads.
- Develop and optimize PySpark jobs on Databricks, ensuring performance and reliability at scale.
- Write complex, well-structured SQL for data transformation, validation, and reporting across SQL Server, Synapse Analytics, and Snowflake.
- Collaborate with data scientists to understand data requirements and translate them into robust engineering solutions.
- Monitor pipeline health, troubleshoot failures, and implement improvements to data quality and observability.
- Document data flows, schemas, and pipeline logic to maintain institutional knowledge.
- Present project progress, architecture decisions, and results to business stakeholders in a clear, concise manner.
- Partner with platform and cloud engineering teams on Azure infrastructure and Databricks workspace management.

Technical Skills

- Python: Expert-level proficiency; strong command of data engineering libraries (pandas, PySpark, SQLAlchemy), code quality practices, and packaging.
- SQL: Expert-level; able to write complex analytical queries, window functions, CTEs, and performance-tune queries across multiple platforms.
- Apache Spark / PySpark: Comfortable developing and debugging distributed data processing jobs; understands Spark execution model and optimization basics.
- Databricks: Hands-on experience with notebooks, Delta Lake, Jobs/Workflows, and cluster configuration.
- Azure: Working experience with core Azure data services (Azure Data Factory, Azure Data Lake Storage, Azure Synapse Analytics, or equivalent).
- SQL Server & Synapse Analytics: Experience querying and loading data; understanding of schema design and indexing strategies.
- Snowflake: Practical experience with Snowflake warehousing, data loading patterns, and role-based access.
- Windows: Comfortable working in a Windows-based development environment.

Education & Experience

- Bachelor's degree in Computer Science, Information Systems, Mathematics, Engineering, or a related quantitative field.
- 2-4 years of professional experience in a data engineering, analytics engineering, or software engineering role with a significant data focus.

Soft Skills

- Strong written and verbal communication skills; able to present technical concepts to non-technical business audiences.
- Collaborative team player who engages proactively with data scientists, analysts, and business partners.
- Detail-oriented with a bias toward clean, maintainable, and well-tested code.
- Comfortable managing multiple priorities in a fast-paced delivery environment.

Nice To Have

- CLI tools: Familiarity with shell scripting, command-line data tools, and working in Linux environments.
- Version control: Experience with Git-based workflows in a team setting.
- Orchestration: Exposure to workflow orchestration tools (e.g., Apache Airflow, Azure Data Factory pipelines, Databricks Workflows).
- Insurance / financial services domain: Prior experience in a regulated industry is a plus but not required.
- CI/CD: Familiarity with automated testing and deployment pipelines for data assets.

The pay range for the role is $82,000 to $92,000. 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.