Infor is hiring a Business Product Owner, Snowflake to own Snowflake as a product across Infor’s Data & AI capability and to be the single business point of view on a platform that many teams build on. Snowflake at Infor serves two demands equally: the data marketplace that distributes certified data products to analysts, and the Cortex and AgentCore data layer that the AI agent runtime reads from. This role sits adjacent to and supporting both, owns the platform’s business case, roadmap, and consumption economics, and holds real decision rights on the platform’s business questions: what gets built on Snowflake, for whom, at what cost, and to what standard.
Infor’s Snowflake footprint is shared. Much of the enterprise’s data engineering already runs on it, so this role owns usage and cost across teams it does not direct, and earns its influence through economics and standards rather than authority. It is the business product owner, paired with a technical product owner on the data side, and it also holds product ownership for the platform’s AI and ML features for now, a deliberate stewardship to be handed off cleanly when a team is ready to own the AI technology directly. The role experiments to find better usage and cost options and is accountable for the results. It is not another builder, and not a gate teams route around: it is the person who makes Snowflake earn its cost and serve everyone who depends on it. The role calls for the judgment and communication of a Principal in a Principle-Based Management environment: comparative advantage, a contribution-motivated mindset, and intellectual honesty under disagreement.
Our Team
Data & AI is Infor IT’s integrated capability for internal AI: Trusted Data Products, AI Engineering & Platform, and Analysis & Engagement. Scope is internal AI for Infor employees and operations, not customer-facing product AI. Operating principles: hold one business point of view on the platform so decisions made in different teams still add up; spend by measured value, and earn influence through economics and standards rather than authority; ship outcomes, not slides. Infor is actively investing in and scaling this capability through 2026 and beyond.
This role establishes single business ownership of Snowflake: one platform point of view so consumption economics, standards, and the calls on where AI and ML workloads run stay consistent across teams. The role holds the whole platform from the business side: the roadmap, the economics, and the standards that the data marketplace, the agent runtime, and the teams across Infor all depend on, coordinated across the data product and AI engineering work rather than collapsed into either.
A Typical Day in the Life Includes:
• Own Snowflake as a product with one business point of view: a published roadmap, a prioritized backlog, and consumption economics as the accountability, spanning the data marketplace and the AI agent runtime equally.
• Own the platform’s economics: cost tied to the capability and to each team that uses Snowflake, anomaly alerts before cost events, run rate tracked to plan, and a financial case for consumption defensible against measured business value.
• Set the platform standards every team builds on: how certified data products are distributed through the marketplace, how approved agents read Snowflake-resident data through a governed interface, and how exploratory work happens without shadow environments. Earn adoption of those standards through value, not mandate.
• Hold product ownership for the platform’s AI and ML features: prioritize and shape the capabilities the teams need (Cortex Analyst, Cortex Search, Cortex Agents, the feature store, the model registry), with a clean handoff designed in for when a team is ready to own the AI technology directly.
• Experiment to find better usage and cost options: recommend where analytics and AI and ML workloads should run with documented economics, and bring the broader organization up the Snowflake learning curve so the platform is used well, not only paid for.
• Own the Snowflake vendor relationship from the business side: consumption economics, pricing structure, and feature roadmap influence, coordinated with the technical product owner on the data side so business and technical ownership stay aligned.
Basic Qualifications:
• Experience in data, analytics, or data-platform product roles including product ownership: has owned a major data or AI platform as a product end to end, with a published roadmap, prioritized backlog, and consumption economics as the accountability. Platform ownership, not only project / product management.
• Snowflake fluency, with hands-on experience working directly on the platform: the data marketplace and certified data distribution, Cortex AI and ML workloads including the agentic surface (Cortex Analyst, Cortex Search, Cortex Agents), the feature store and model registry, governed agent-to-data access (MCP or equivalent), and exploratory workspaces. Conversant enough to keep business and technical ownership aligned with a technical counterpart.
• Real command of consumption economics and FinOps for vendor-managed, usage-priced platforms: anomaly handling, threshold management, run rate to plan, and a case for spend defensible against measured business value.
• Has owned a platform used by teams the owner did not control: earned adoption of standards through value rather than authority, with influence across data engineering and AI engineering audiences without direct reports.
• Experience recommending where AI and ML workloads should run: comparing options from measured value (Cortex versus external ML platforms, for example), with recommendations that held.
• Experience with platform advocacy leading to high value outcomes: raised an organization’s fluency on a platform so it was used well, not only paid for; clear articulation of numerous high value outcomes as a result.
• Legal authorization to work permanently in the United States for any employer without requiring a visa transfer or visa sponsorship now or in the future.
Preferred Qualifications:
• Experience building or operating an enterprise data marketplace, not only data warehouses.
• Comparison experience across Data, ML and AI platforms (Databricks, relevant cloud Microsoft and Amazon cloud services) strong enough to make build-and-run recommendations from measured value.
• Background in certified data product standards, MDM, or data governance at platform scale.
• Experience standing up feature stores and model registries that engineering teams actually adopted.
• Has held a business product owner role paired with a technical counterpart and made that split work.
• Experience with LLM platforms (Anthropic Claude, Microsoft Copilot Studio, or peer) reading from Snowflake-resident data.