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.

You’ll play a pivotal role in modernizing our data environment and operationalizing AI across a decentralized organization.

 

 

Key responsibilities: 

• Build strong relationships with cross-functional partners, regularly communicating progress, insights, and alignment between data strategies and business goals
• Ability to operate 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

 

Preferred Experience & Qualifications

• 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.) Experience transitioning from a fragmented legacy data environment to cloud-based medallion architecture
• Experience with ERP, AP, and CRM systems (such as Sage 100, QuickBooks, Acumatica, MuleSoft, Salesforce)
• Experience with data governance frameworks
  • Familiarity with Purview or Unity Catalog 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.

 

Required Skills: 

• 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
  • Implement data standards practices and enforcement
  • Establish clear data classification and access controls
  • Lead overall data stewardship including defining roles and responsibilities across the organization
  • Ensure traceability, transparency, and consistency of enterprise data outputs
• Enterprise Data Platform technical proficiency – including:
  • Own the Enterprise Data Roadmap, order and prioritization, dependencies, and timelines easily accessible by stakeholders
  • Create & implement framework to prioritize ingestion, transformation, and reporting of data sources
  • Retire legacy systems and data transformation workflows and ensure complete and accurate transition to new data environment
  • Oversee testing and QA
  • Create and monitor the automation of alerts, logs, and Lakehouse availability reporting
• AI Strategy & Execution proficiency – including:
  • Systems-based thinking and focus on value creation
  • Establish Enterprise AI frameworks, approach, and guardrails
  • Translate enterprise AI strategy into executable use cases and initiatives
  • Assist leadership to prioritize AI investments and manage delivery
  • Partner with IT in testing and exploration in sandbox environments
  • Evaluate and mitigate risks associated with the use of ‘Shadow AI’ across the Enterprise

Sche