Claims Data Analytics Engineer

At GEICO, we offer a rewarding career where your ambitions are met with endless possibilities. Every day we honor our iconic brand by offering quality coverage to millions of customers and being there when they need us most. We thrive through relentless innovation to exceed our customers’ expectations while making a real impact for our company through our shared purpose. When you join our company, we want you to feel valued, supported and proud to work here.

That’s why we offer The GEICO Pledge:
Great Company, Great Culture, Great Rewards and Great Careers.

Position Summary The Government Employees Insurance Company (GEICO) is a private American auto insurance company with headquarters in Chevy Chase, Maryland. GEICO is a wholly owned subsidiary of Berkshire Hathaway and is the third largest auto insurer in the United States. In 2025, GEICO earned premiums worth over $40 billion U.S. dollars. GEICO is going through a massive digital transformation to re-platform the Insurance industry, removing friction across Customers, Partners, Marketplace, Segments, Channels, and Experiences as we grow our reach and market share.

About The Role GEICO seeks a Claims Data Analytics Engineer to join our Claims Innovation team. You will diagnose complex business challenges, recommend strategic solutions, and collaborate across work streams to drive innovation. Claims Data Analytics Engineers play a critical role in building scalable, reliable, and insightful data that enables advanced analysis to shape strategy, deliver actionable intelligence, and support critical decision-making for senior leaders across Operations.

This role blends data engineering, analytics, and solution design—requiring strong technical skills including SQL, Snowflake experience, Python proficiency, and a curious, analytical mindset.

At the forefront of GEICO’s Operations transformation, Claims Data Analytics Engineers in Claims Innovation have opportunities to revolutionize our analytic capabilities to succeed against executive-level challenges with enterprise-level impacts. You will collaborate across Claims Innovation to create transform raw data into production-ready analytics assets and blend data engineering, analytics, and solution design through strong technical skills (SQL, Snowflake, Python, Power BI) driving team and stakeholder success.

This role is an active partner in continual improvements and maturation of our analytics ecosystem, setting a benchmark for excellence.

• Build, maintain, and optimize Python-based data processing workflows, including ETL/ELT pipelines, automation scripts, and analytical models.
• Engineer and optimize data models, views, and warehouse structures in Snowflake to support analytical performance and data governance.
• Write advanced, efficient SQL to transform Claims data and implement reusable data patterns.
• Design and develop Power BI dashboards, metrics layers, and semantic models to support reporting and business insights.
• Apply deep data structure knowledge and strong analytical thinking to translate complex Claims data and customer-driven requirements into clear, actionable analytics and production-ready solutions.
• Ensure data integrity, security, and compliance with organizational standards.
• Manage workload in a Sprint environment with competing priorities.

• Work with Operations customers to structure and translate requirements into data needs with clear pathways and milestones to deliver high-impact products.
• Lead cross-functional collaboration across Operations and other engineering groups throughout the engineering lifecycle—from requirements through deployment.
• Mentor junior engineers and analysts, sharing best practices for coding, analytical methodology, and solution design.
• Consistently contribute to improving engineering processes, documentation, automation, and team standards.

• Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or related field—or +5 equivalent experience.
• Expert-level SQL (query optimization, windowing functions, large dataset operations, query performance…