Senior Data Engineer (P&C commercial insurance data) US CLIENT

Position Summary

Preference will be given to candidates with strong experience on Databricks and modern lakehouse architecture.

• Design, develop, and maintain scalable data pipelines and ETL/ELT processes to integrate data from policy admin systems, rating engines, billing, claims, and third-party data providers.
• Lead end-to-end data mapping for MGA commercial lines programs (e.g., GL, Property, Auto, Package, Excess/Umbrella), including source-to-target mapping, transformation rules, and data lineage documentation.
• Build and optimize data models and lakehouse structures to support reporting, analytics, and downstream applications (e.g., underwriting workbenches, dashboards, actuarial models).
• Implement best practices for data quality, including validation rules, reconciliation checks, and exception handling for policy, premium, exposure, and claims datasets.
• Collaborate with product, underwriting, and operations teams to translate business requirements into robust data solutions supporting bordereaux, regulatory reporting, and carrier reporting obligations.
• Develop reusable data assets, reference data, and canonical models for MGA programs to enable faster onboarding of new products, carriers, and distribution partners.
• Monitor and troubleshoot production data pipelines; ensure performance, reliability, and adherence to SLAs.
• Contribute to data governance efforts, including metadata management, data cataloging, access controls, and compliance with security/privacy standards.
• Mentor junior data engineers and contribute to coding standards, best practices, and technical design reviews.

1. Bachelor’s degree in Computer Science, Information Systems, Engineering, Mathematics, or related field (or equivalent experience).
2. 5+ years of professional experience in data engineering, with a minimum of 3+ years focused on P&C commercial insurance data.
3. Demonstrated experience working with MGAs or delegated authority environments (e.g., carrier, program administrator, or insurtech supporting MGAs).
4. Strong expertise in data mapping and integration for policy, billing, and claims systems, including complex source-to-target mappings and transformation logic.
5. Proficiency in SQL and at least one programming language commonly used for data engineering (e.g., Python, Scala).
6. Hands-on experience with modern data platforms (warehouses or lakehouses), ETL/ELT tools, and workflow orchestration.
7. Solid understanding of data modeling (relational and dimensional), performance optimization, and handling large-scale structured and semi-structured data.
8. Experience working in Agile environments and collaborating with cross-functional teams (product, engineering, underwriting, analytics).

1. Strong hands-on experience with Databricks (Delta Lake, Spark, notebooks, jobs) and related cloud services (e.g., Azure, AWS, or GCP).
2. Experience designing and operating a lakehouse architecture to support self-service analytics and advanced analytics/data science.
3. Familiarity with common commercial insurance and MGA data standards (e.g., ACORD, bordereaux structures, carrier reporting schemas).
4. Experience integrating with policy administration systems, rating engines, and other insurance core systems.
5. Background working with BI tools (e.g., Power BI, Tableau, Looker) and supporting analytics/actuarial teams.
6. Exposure to data governance frameworks, data catalog tools, and privacy/security best practices in regulated environments.