Sr. Director, Trusted Data Products

Infor is hiring a Senior Director, Trusted Data Products to own the data foundation of its integrated Data & AI capability. The role sets the certified data product standard, leads the reorientation from MDM as organizing principle to AI-aligned data products consumed widely across Infor, and is accountable for the Data Quality Score lift, certified data product publication, and the data foundation other AI and analytics work depends on. The person in this role approaches building valued and trusted data products as a principled entrepreneur, surfacing the product through the Snowflake Marketplace.

The role calls for the judgment and communication of an accountable executive in a Principle-Based Management environment: comparative advantage, a contribution-motivated mindset, and intellectual honesty under disagreement.

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: govern the data product for a high quality data representation of our business, making recommendations to upstream system owners when and where helpful; lead team members by establishing a clear vision and a way to contribute toward achieving it through their comparative advantage; ship outcomes, not slides. Infor is actively investing in and scaling this capability through 2026 and beyond.

Trusted Data Products owns the data foundation everything else depends on. AI output compounds value only when the data underneath is trustworthy, reusable, and actually consumed by the people and systems that need it. The Senior Director sets the certified data product standard, runs the reorientation, and is accountable for data products used at scale to contribute clear value, not only system integrations or data catalogs for audit.

* Own the enterprise data foundation: data strategy, quality, metadata, lineage, governance, certification, communication, reporting, and informing development plans for enterprise data engineering, as a united data capability.

* Set and operate the Certified Data Product standard, built to be consumed; for example, a Copilot or Claude user can find what they need in Snowflake to analyze across multiple systems and reach a sound conclusion. Own unit testing and collaborate with AI teams to ensure AI products return accurate results through MCP connections.

* Deliver multiple Certified Data Products published by year-end, prioritized by business value and measurable usage.

* Lift and sustain the Data Quality Score: publish baselines, improve measures quarterly, and trace issues to root cause and owner with platform owners.

* Own the adoption mechanism, not just publication: data products land with the work that consumes them. Documentation, enablement, and change management for the analyst and engineering populations that need to switch from local extracts to certified products.

* Drive process transformation and dynamic schema creation: redesign how data gets produced, certified, and consumed so good data emerges from how the work is done, not from rework layered on top.

* Make the Snowflake Data Marketplace our most trusted certified data product distribution mechanism, so platform decisions and data product decisions stay coordinated.

* Coordinate cross-pillar with Finance, RevOps, Global Professional Services, R&D, Customer Success, and Solution Delivery so the data products produced match the work that depends on them.

* Maintain the data privacy and security posture appropriate to a software company: employee data, customer and prospect data, and regulatory frameworks (GDPR, CCPA, SOC 2 boundaries) coordinated with Infrastructure and security functions.

* Experience in data and analytics, including leadership at the Director level or above, prior Senior Director, and experience leading through influence across peer functions without direct authority.

* Consultative process transformation experience: has redesigned how data gets produced, certified, and consumed rather than adding pipelines on top of broken ones, with MDM experience deep enough to judge when workflow changes at the source system is the right answer and when de-emphasizing it for process transformation or dynamic schema creation yields better data enabled outcomes.

* Experience at an enterprise software company (or a company selling software-adjacent products to businesses) in delivering data products consumed widely.

* Hands-on Snowflake experience operating, coaching, and leading at scale on the platform: depth to lead with credibility, stay conversant across both strategy and the technology, and inform and develop teams beyond your own. Especially relevant is depth of understanding about Cortex and validating MCP-enabled access results in accurate data and results.

* Data security and authentication strategy and implementation. Understanding of best and scalable strategies to ensure data is accessed and controlled securely through respect of permissions and authorities identified via source systems including Entra groups and other pass-through infrastructure.

* Hands-on data modeling fluency (dimensional modeling and slowly changing dimensions, for example); SQL experience to personally inspect and pressure-test what the team produces; experience measuring and improving data quality health in line with stakeholder expectations.

* Working AI fluency: tool-using LLM agents, retrieval-augmented generation patterns, semantic clarity in schemas, and current protocols (Model Context Protocol as an example). The role operates inside an integrated AI and data capability at a large enterprise software company; AI is the daily working context.

* Experience defining a semantic layer or data contracts as the stable, agent-ready interface to data products; for example, a knowledge graph or semantic model that makes data AI-ready.

* Experience collaborating with an AI platform team where data products were tuned for AI agent consumption (tool use, prompt-aware schema design, embedding and retrieval strategy).

* Data product adoption or change management program leadership with documented business outcomes (cycle time, decision quality, cost reduction).

* Track record of retiring legacy scope: data products, ETL, or MDM retired rather than only added to.

* Data Marketplace operation experience at scale (Snowflake or peer).

* LLM platform integration experience: AWS Bedrock, Anthropic Claude, Microsoft Copilot, or peer platforms reading from Snowflake-resident data.

* Acquisition data integration experience.