Data Product Manager

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.

• 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.