Enterprise Data & AI Strategy Manager

Enterprise Data & AI Strategy Manager

IBP is seeking an Enterprise Data & AI Strategy Manager to accelerate our digital evolution. Reporting to the VP of Internal Audit, this role serves as a strategic connector—aligning business units, PMO, IT, and leadership to deliver scalable data solutions, AI‑enabled insights, and enterprise automation.

• Build strong relationships with cross‑functional partners, regularly communicating progress, insights, and alignment between data strategies and business goals.
• Operate successfully in a highly decentralized environment.
• Partner with IT, data stewards, and business unit leaders to define evolving data requirements.
• Enforce data governance frameworks, standards, and policies to ensure consistency, compliance, and data integrity.
• Monitor and promote data integrity across systems.
• Support data remediation by leveraging AI‑driven tools for gap‑filling, correcting, matching, and auditing data, ensuring data quality and consistency across systems.

• 8+ years of experience in data, analytics, and enterprise transformation roles.
• Demonstrable experience in creating & modernizing enterprise data reporting frameworks & supporting departments.
• Experience working on hyperscalers (Azure, AWS) and with cloud data warehousing platforms (Fabric, Databricks, Snowflake, etc.) and transitioning from a fragmented legacy data environment to cloud‑based medallion architecture.
• Experience with ERP, AP, and CRM systems (such as Sage 100, Quick Books, Acumatica, Mule Soft, Salesforce).
• Experience with data governance frameworks.
• Familiarity with Purview or Unity Catalog is a plus.
• Create and scale intelligent autonomous agents that provide value‑add, goal‑driven automation experiences.
• Enable and execute multi‑agent workflows across systems, enhancing decision‑making and workflow adaptability.
• Strong preference for successful AI rollouts to production.
• Support data remediation by leveraging AI‑driven tools for gap‑filling, correcting, matching, and auditing data, ensuring data quality and consistency across systems.

• Analytical & problem‑solving abilities: strong analytical skills to identify trends, solve complex issues, and translate insights into actionable strategies.
• Communication skills: strong verbal and written communication skills, with the ability to clearly convey complex data concepts to non‑technical audiences.
• Relationship building & business engagement: strong interpersonal skills to effectively collaborate with cross‑functional teams, influence decision‑making, and drive alignment/adoption of solutions across a decentralized branch network.
• Data Governance development & adoption, including:
  • Knowledge of data governance frameworks, data quality management, and compliance practices to ensure data integrity and security.
  • Implementing data standards practices and enforcement.
  • Establishing clear data classification and access controls.
  • Leading overall data stewardship including defining roles and responsibilities across the organization.
  • Ensuring traceability, transparency, and consistency of enterprise data outputs.
• Enterprise Data Platform technical proficiency, including:
  • Owning the Enterprise Data Roadmap, order and prioritization, dependencies, and timelines easily accessible by stakeholders.
  • Creating & implementing framework to prioritize ingestion, transformation, and reporting of data sources.
  • Retiring legacy systems and data transformation workflows and ensuring complete and accurate transition to new data environment.
  • Overseeing testing and QA.
  • Creating and monitoring the automation of alerts, logs, and Lakehouse availability reporting.
• AI Strategy & Execution proficiency, including:
  • Systems‑based thinking and focus on value creation.
  • Establishing Enterprise AI frameworks, approach, and guardrails.
  • Translating enterprise AI strategy into executable use cases and initiatives.
  • Assisting leadership to prioritize AI investments and manage delivery.
  • Partnering with IT in testing and exploration in sandbox environments.
  • Evaluating and mitigating risks associated with the use of ‘Shadow AI’ across the Enterprise.

Hybrid