Department Manager, Business Data Engagement

Position Summary

Lead the enterprise Business Data Engagement function and the Portfolio Data Manager (PDM) model. Reporting to the Managing Director, Data & AI, this leader is one of three Data & AI leadership pillars and the accountable owner of the Data & AI front door: shaping demand, strengthening executive sponsorship, connecting business priorities to delivery capacity, driving adoption, and ensuring realized value is walked back to business leadership. This is an internal-only role. It requires an established network, deep knowledge of how the Global Practices (GPs), Regions, and COR operate, and the credibility to influence senior business leaders from day one.

Key Responsibilities

Team & Business Relationships

• Lead, coach, and performance-manage the three initial Portfolio Data Managers and establish common engagement, demand-shaping, adoption, and value-realization practices.
• Build trusted relationships with GP, COR, and 1898 & Co. leaders and ensure each portfolio has a clear Data & AI engagement path.
• Represent Data & AI in relevant business planning and portfolio discussions while operating through influence rather than direct authority over the GPs.
• Coordinate the Data & AI front door with the Technical Solutions Group’s (TSG) business-partner intake (Corporate Innovation) so business units experience one coherent path: technology solution requests route to TSG; data, analytics, and AI demand routes through the PDMs into the Shared Business Backlog, with cross-referral in both directions.

Demand & Front Door

• Ensure requests are framed as business problems with outcomes, executive sponsorship, committed SME capacity, adoption plans, and measurable value before significant delivery capacity is committed.
• Own demand quality at the front door and work with the Director, Operating Model & Strategy to place qualified demand into the Shared Business Backlog.
• Partner with the Director, Data Engineering & AI to match prioritized demand to feasible technical capacity and to manage business expectations transparently.
• Identify common needs across portfolios and advocate reuse before local duplication.

Adoption & Value

• Drive business readiness, UAT sponsorship, change/adoption, and post-release use so solutions become part of business decisions and processes.
• Walk delivery outcomes and Finance-validated value back to sponsors and portfolio leadership; use evidence to shape future demand.

Data Management & Stewardship

• Establish and enforce consistent data management practices across the PDM team (data definitions, ownership/stewardship, data quality standards, and lifecycle expectations) aligned with enterprise data governance policies.
• Ensure business data entering the Data & AI delivery pipeline meets enterprise data quality, security, privacy, and compliance standards before delivery capacity is committed.
• Partner with business data owners and stewards across GPs, COR, and 1898 & Co. to strengthen master/reference data consistency and to embed data stewardship and data literacy across the business.

Stakeholder Influence

• Operate as a trusted advisor to GP, COR, and 1898 & Co. executives, shaping priorities and investment decisions through credibility and evidence rather than positional authority.
• Build and sustain active executive sponsorship for Data & AI investments, securing sponsor ownership, SME capacity, and adoption commitments before delivery begins.
• Constructively challenge low-value, duplicative, or poorly framed demand while preserving trusted relationships with business leadership.
• Hold self and the PDM team accountable for delivering on commitments made to the business and for walking Finance-validated value back to sponsors.

What Success Looks Like (First 12 Months)

• Team: Three PDMs in place and performing, with common engagement, demand-shaping, adoption, and value-realization practices in use.
• Engagement: Every portfolio has a defined Data & AI engagement path and an executive sponsor relationship
• Front door: All demand entering the Shared Business Backlog meets front-door criteria: business problem, outcomes, sponsor, committed SME capacity, adoption plan, and measurable value.\\
• One path: A single intake path with TSG operating, with cross-referral in both directions and no confusion for business units about where to go.
• Adoption and value: Adoption and post-release use tracked for delivered solutions, and Finance-validated value walked back to sponsors for priority initiatives.
• Data quality: Data definitions, stewardship, and quality standards applied to business data before delivery capacity is committed. Targets to be set with the Managing Director at the start of the role.

• Bachelor Degree in Information Technology, Computer Science, or related degree from an accredited program and 8 years relevant experience required or Associate Degree and 10 years relevant experience required or High School Diploma/GED and 12 years relevant experience required
• Demonstrated ability to influence senior leaders, shape ambiguous demand, challenge priorities constructively, and build trust without direct authority.
• Strong commercial and operational acumen; able to translate business problems into measurable outcomes and investment-ready use cases.
• Experience with AI, analytics, and data modeling, including hands-on exposure to modern data and AI platforms such as Databricks, Azure, AWS, GCP, Palantir, and Azure AI Foundry.
• Experience leading relationship managers, product owners, portfolio leads, business partners, or similar roles.
• Strong communication, facilitation, change/adoption, and executive storytelling skills.
• Proven stakeholder engagement and delivery leadership: able to build and sustain senior executive relationships across the enterprise and reliably deliver committed business outcomes end to end.
• Proven ability to work directly with business customers in shaping value (defining outcomes, use cases, and success measures) and to deliver material, measurable impact to the business P&L by leveraging data.
• Ability to deal with ambiguity: comfortable operating, deciding, and delivering in a fast-evolving environment.
• Professional experience in business leadership, engineering/EPC operations, digital/data transformation, portfolio/product leadership, or a comparable business-facing role; meaningful leadership experience.