GEICO is seeking a Product Manager to support their Data Platform, focusing on driving product direction for data services and tools. The role emphasizes the intersection of product management, data platforms, analytics, and AI. The ideal candidate will translate complex business needs into actionable product capabilities that enhance data trust and usability. This position requires collaboration with various stakeholders to ensure the delivery of effective platform capabilities.

• 5+ years of product management experience building data, analytics, AI, developer tooling, or internal platform products.
• Strong understanding of modern data architectures, data modeling, BI concepts, cloud data ecosystems, query engines, compute frameworks, APIs, SDKs, or platform integration patterns.
• Experience translating complex business and technical requirements into clear product requirements, roadmaps, user stories, and measurable outcomes.
• Proven ability to work closely with engineering, data, analytics, governance, security, and business stakeholders on technically complex products.
• Working knowledge of semantic layers, governed metrics, metadata, ontology concepts, or business glossary practices as enabling capabilities within a broader data platform.
• Working knowledge of AI/ML and generative AI concepts, including LLM capabilities and limitations, natural language query, evaluation, and responsible use of AI in governed data environments.
• Experience using instrumentation, telemetry, observability, customer feedback, and product analytics to make data-driven product decisions.
• Excellent communication, stakeholder management, prioritization, collaboration, and problem-solving skills.

• Support the product vision, roadmap, and execution for data platform services, tools, and capabilities.
• Translate business and technical needs into clear product requirements and user-focused capabilities.
• Partner with engineering, data architecture, governance, analytics, and business stakeholders to deliver platform capabilities.
• Use semantic layer and ontology concepts to improve shared business terminology and cross-domain interoperability.
• Help enable natural language analytics and AI-powered data experiences.
• Drive adoption through stakeholder engagement, customer roadshows, training, documentation, and feedback loops.
• Define and track product success metrics including adoption, self-service usage, and platform reliability.
• Stay informed on modern data platform trends to inform product strategy.