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