Mid-Level/Lead DA Engineer (GRAPH DATABASE ENGINEER): HYBRID at State Farm in Bloomington, IL

Job description

Overview Being good neighbors – helping people, investing in our communities, and making the world a better place – is who we are at State Farm. It is at the core of how we operate and the reason for our success. We're moving with urgency to become a Next Gen Good Neighbor, with technology that strengthens human connections and enhances the customer experience. As an employee, you'll spend less time navigating systems and more time applying judgment, empathy, and expertise in the moments that matter most. Hybrid Work Arrangement: A “Hybrid” position means selected candidates will be assigned to a facility and will spend some time working both in the office and from home. For Enterprise Technology, a hybrid work arrangement will require approximately 40% in-office time each month, beginning in late 2027. HUB LOCATIONS: Richardson, TX; Dunwoody, GA; Tempe, AZ; Bloomington, IL SPONSORSHIP: Applicants for this position are required to be eligible to lawfully work in the U.S. immediately; employer will not sponsor applicants for U.S. work authorization (e.g. H-1B visa) for this opportunity. Responsibilities We are seeking an experienced DA Engineer (GRAPH DATABASE ENGINEER) to join our Claims Knowledge Graph team. This team builds graph-based data solutions that support claims investigations, including the identification of potential fraud, identity theft, and injury-related insights. As a Graph Database Data Engineer, you will independently design, build, and optimize knowledge graphs that connect claims data and reveal relationships across entities. This is an exciting opportunity to lead graph architecture and production deployment, develop reliable data pipelines, and apply graph analytics to support investigation and decision-making. Success in this role requires strong technical ownership and effective teamwork. You will collaborate with business partners, data analysts, data engineers, infrastructure engineers, and software engineers to translate business needs into scalable, production-ready graph solutions. Some Responsibilities May Include Graph architecture and data modeling: Lead architectural decisions and design and maintain labeled property graph models, including entities, relationships, properties, and constraints, aligned with claims investigation use cases. Data integration: Build and maintain scalable pipelines to ingest, transform, validate, and load data from relational databases and other structured and unstructured sources into graph databases. Graph database engineering and deployment: Lead the implementation, configuration, and production deployment of graph database solutions, such as Neo4j or Amazon Neptune, in collaboration with infrastructure teams. Performance optimization: Optimize graph queries, indexes, data models, and ingestion processes to improve performance, scalability, and resource utilization. Graph analytics: Apply algorithms such as community detection, centrality, and pathfinding to identify relationships, patterns, and investigative insights. Production reliability: Implement automated testing, monitoring, data quality controls, and operational documentation to support reliable graph solutions. Security and governance: Apply organizational standards for data access, privacy, security, and governance. Cross-functional collaboration: Translate business requirements into technical designs and communicate findings, tradeoffs, and recommendations to technical and business audiences. Qualifications We Are Seeking Candidates With: At least five years of professional data engineering experience. Hands-on experience developing and supporting graph database solutions using Neo4j, Amazon Neptune, or a comparable platform. Demonstrated ability to work independently and lead graph architecture and production deployment. Experience designing labeled property graph models and writing and optimizing graph queries using a language appropriate to the platform, such as Cypher or Gremlin. Proficiency in SQ