Data Product Manager

The Data Product Manager will help AHEAD turn its data into trusted, discoverable and reusable products that solve business problems. Reporting to the Director, Data Platform, this role works with Data Platform engineering, Analytics and domain teams to set priorities, define requirements and help consumers use data effectively. This is a hands‑on individual‑contributor role. You will work directly with consumers, examine existing data assets, define useful products, maintain product documentation and catalog content, and help teams deliver and improve those products throughout their lifecycle. Governance is an essential part of the role: making sure consumers understand what a product means, whether they can trust it, and how to access and use it appropriately. Your governance responsibilities cover data products delivered or published through the Data Platform, within existing enterprise policies. You will apply agreed policies through practical product standards and discovery workflows, partnering with engineering, Security and domain owners on supporting controls. Duties and Responsibilities Product discovery, strategy and prioritization Work with business stakeholders, analysts, application teams and other data consumers to understand their decisions, workflows and unmet needs; distinguish a reusable data product opportunity from a one‑off reporting or integration request. Define product problems, target consumers, expected value and success measures, evaluating existing assets for reuse before proposing new development. Own the data product roadmap and product priorities within the strategy and capacity agreed with the Director, Data Platform; balance business value, urgency, risk, dependencies and ongoing support costs. Own product goals, priorities and requirements; partner with the Product Owner, who manages day‑to‑day backlog coordination, intake and delivery dependencies, using one shared backlog. Product delivery and lifecycle Translate consumer needs into engineering‑ready requirements and acceptance criteria covering business definitions, data scope and granularity, freshness, quality, access and supported consumption methods. Partner with Data Platform engineers, Analytics and source‑system teams to scope practical releases, resolve dependencies, validate outputs with consumers and communicate delivery trade‑offs. Manage the product lifecycle from discovery through launch, adoption, improvement and retirement, including change communication and the impact of schema or definition changes on consumers. Partner with Analytics and domain owners to identify authoritative business definitions and align reusable datasets and semantic models, preserving useful existing capabilities and resolving inconsistencies where they matter. Governance and product trust Define and apply lightweight product publication and readiness standards, including accountable owners, business definitions, classification, source and lineage information, quality and freshness expectations, access instructions and lifecycle status. Establish named business/domain owners who remain accountable for data meaning, quality expectations and appropriate‑use or access decisions within policy. Coordinate stewardship and document those decisions. Define governance and access requirements with Security, IAM and engineering, including approval paths, sensitive‑data protections and traceability; validate that delivered products meet the agreed requirements. Prioritize quality and governance improvements based on consumer impact and risk. Engineers implement the supporting checks, monitoring, lineage capture and technical controls. Discovery, self‑service and adoption Maintain a useful inventory of data products and directly create or configure catalog entries, glossary terms, product documentation and onboarding workflows using available tools. Own the consumer experience requirements for finding, understanding and requesting access to trusted data, including business‑friendly discovery for users who do not work directly in Snowflake. Evaluate existing catalog and metadata capabilities, identify practical gaps and prioritize incremental improvements before proposing new tools or custom development. Support launch communications and consumer onboarding; use feedback and practical indicators of usage, quality and access friction to guide improvements. Education and Experience Bachelor's degree or equivalent practical experience. Five or more years of relevant experience across data product management, analytics, data platforms, analytics engineering, data governance or related disciplines, with experience shaping priorities, requirements or cross‑functional delivery; a formal product management title is not required. Practical experience helping deliver and improve data products or platform capabilities in response to consumer needs. Strong skills in consumer discovery, problem definition, prioritization, requirements, acceptance criteria and stakeholder alignment. Technical fluency in cloud data platforms, pipelines, data modeling, semantic layers and consumption patterns, with working SQL skills sufficient to investigate data and validate product behavior. Practical experience applying ownership, metadata, lineage, quality, classification and access requirements to data products, and maintaining product documentation, catalog content or discovery workflows. Ability to translate between business and technical audiences, negotiate competing priorities and influence teams without direct authority. Ability to make progress independently in a small, resource‑constrained team, using lightweight processes and balancing new capabilities with operational commitments. Preferred qualifications Experience with Snowflake, dbt and Power BI, or comparable cloud data and analytics platforms. Hands‑on experience configuring or onboarding assets into a data catalog, business glossary or metadata management tool; no specific vendor is required. Experience improving governed self‑service access in partnership with Security and IAM teams. Experience supporting shared business definitions and reusable semantic models across teams or domains. Familiarity with data products consumed through APIs or AI tools, including their access, quality and traceability considerations. What success looks like Reusable data products are delivered, adopted and demonstrate measurable business value, such as time saved, reduced manual work or improved reporting accuracy. The team has a prioritized, achievable product roadmap tied to clear consumer problems and business outcomes. Consumers can find important products, understand their meaning and limitations, and identify how to obtain appropriate access. Published products have clear ownership and proportionate quality, metadata and access expectations. Analytics, engineering and business owners use an agreed approach to shared definitions and product changes. Consumer feedback and practical usage, quality and access indicators inform product improvements. #J-18808-Ljbffr AHEAD