Data Domain Lead
Chicago, IL - USA
Job Summary
is seeking aData Domain Leadto drive the strategy development and adoption of enterprise data products within an assigned domain. This role will serve as the single accountable leader for turning domain data into trusted reusable and business-ready products that support operational processes analytics reporting and AI use cases.
The ideal candidate will combine strong business acumen with data leadership experience partnering across business technology architecture governance and regional teams to define domain priorities establish authoritative business definitions and deliver measurable business value. This is a highly cross-functional role that requires a product mindset strong stakeholder engagement and the ability to translate business needs into scalable governed data solutions.
- A strategic opportunity to shape enterprise data products that support critical business processes and decisions
- Visibility across business technology and regional leadership teams
- A highly collaborative role working across architecture engineering governance and operational stakeholders
- The ability to influence how data is trusted reused and activated across the organization
- An environment focused on business outcomes measurable value and enterprise-scale transformation
- A key leadership role in advancing Marsh McLennans enterprise data strategy
- The opportunity to drive real business impact through data product adoption and value realization
- Collaboration with global colleagues and cross-functional teams
- Exposure to enterprise architecture governance analytics and AI initiatives
- A culture that values innovation collaboration and continuous improvement
- Own the strategy roadmap and measurable outcomes for an assigned data domain
- Define domain scope boundaries and authoritative business definitions
- Translate business needs into reusable governed domain data products
- Partner with business sponsors architects engineers and governance teams to deliver domain capabilities
- Manage the domain backlog release priorities and product acceptance criteria
- Ensure the domain supports end-to-end business processes not just individual systems or datasets
- Lead tracer testing across real business transactions to validate data quality handoffs identifiers and usability
- Drive adoption across operational applications reporting analytics self-service and AI capabilities
- Establish quality governance privacy and control requirements within the product lifecycle
- Coordinate delivery across global and regional teams including dependencies milestones and risks
- Measure domain success through trust reuse adoption and business value
- Escalate unresolved issues related to ownership quality capacity and priorities
- Significant experience in data products enterprise data business data strategy product management or domain leadership
- Strong knowledge of business process design and translation of business needs into data requirements
- Experience delivering data capabilities into production and driving adoption with business stakeholders
- Working knowledge of data architecture data modeling metadata quality governance and integration
- Strong stakeholder management skills across business technology operations and regional teams
- Proven ability to lead cross-functional initiatives without relying solely on direct authority
- Strong prioritization problem-solving and decision-making skills
- Excellent communication skills with the ability to explain complex data topics in clear business language
- Ability to operate effectively in ambiguity while driving measurable outcomes
- Experience in insurance risk benefits financial services or another complex regulated industry
- A product-oriented mindset with a strong focus on adoption value realization and business outcomes
- Experience balancing enterprise consistency with regional and business-specific requirements
- Demonstrated success in identifying and resolving data handoff ownership and quality issues across end-to-end processes
- Familiarity with data activation across analytics reporting operational platforms and AI use cases