SVP, Head of Data Management

Fidelity•Boston, MA

About The Position

As the Head of FI Data Management, you blend deep understanding of data management practices with a dedication to enabling insights and data-driven decision making that will unlock business value from data across a wide range of FI business functions. As a function in FI, you will define and evolve your team’s vision, strategy, and mission, and create a highly integrated plan for Data Management—aligned to the business’s analytics and insights priorities and the Enterprise data strategy—ensuring that FI data is in the best possible ready state to address current and future business needs. You will also ensure FI data is use-case ready, AI-ready, at scale, recognizing that while LLM capabilities are increasingly commoditized, data is the differentiator —and today’s data is not consistently fit-for-purpose for AI use cases. You will establish pragmatic AI-readiness roadmaps, prioritize use cases, and build the governance, ownership, stewardship, and quality mechanisms required to make high-priority data domains reusable for AI-enabled outcomes. The outcomes that you will drive: More unified view of structured and unstructured data. Data is well documented and easy to search. There are well understood processes and sufficient resources for addressing quality issues. Data as a product: FI data is treated and advanced as a strategic product Data is ready for current and future use cases. There is clear accountability for who delivers on each consumer’s data needs. Key data domains meet defined AI-readiness standards (quality, completeness, lineage, governance, machine-readable semantics, ontology) and can be reused across use cases. Time-to-market to deliver use case ready (including AI-ready) datasets materially improves through repeatable curation patterns and clear accountability (e.g., moving from months to weeks for priority domains). Institutionalized retention and reuse of learnings from initiatives and small-scale tests so the organization compounds advantage instead of relearning the same lessons repeatedly. Alignment with Enterprise Data team on data strategy, roadmaps and execution.

Requirements

Bachelor’s Degree and 10-15+ years of experience in business intelligence, data strategy and data management.
Financial services experience preferred.
Advanced understanding of data management frameworks, tools, platforms and demonstrated history of owning solutions including developing data platforms and leading data governance, and experience developing Artificial Intelligence solutions preferred.
Seasoned leader with a large organization experience with ability to navigate complex organizational structures and drive cross-functional collaboration.
You are a committed leader who serves as an inspirational people leader, capable of setting strategic direction and encouraging others to deliver results and impact to achieve that strategy.
You have senior level experience defining strategic roadmaps and leading the development of solutions and offerings aligned with and in support of the strategic direction of the business.
You have strong communication, collaboration and influencing ability, with demonstrated experience working across L2 stakeholders and businesses with ability to align differing opinions.
You take a data-focused approach to decision making, in day-to-day leadership, engaging with clients/ stakeholders to assist in articulating our road map and aligning that to product priorities and requirements.

Responsibilities

Set and communicate the Data Management vision, strategy, and priorities in partnership with Insights & Analytics; align roadmaps to business outcomes and the Enterprise data strategy.
Own large-scale org transformation across Data Management—driving adoption of new ways of working with Product and Technology (operating model, prioritization/delivery mechanisms, cross-functional decisioning) to improve speed, transparency, and outcomes.
Lead performance management through clear objectives and KPIs; continuously optimize capacity, squads, and delivery by removing impediments and making trade-offs where needed.
Build team capability through intentional skill development across technical depth, AI literacy, use case valuation, business architecture, and executive-ready communication.
Assess industry and client trends; drive enterprise alignment through collaboration, knowledge sharing, and breaking down silos.
Continue to build and run a strong data quality practice, with clear processes, resourcing, and accountability for issue remediation.
Establish and enforce data governance and stewardship standards so data is discoverable, well-documented, and reliably reusable across FI.
Own strategic data product stewardship, including data ownership models, vendor management, and controls for regulatory/ethical use; partner across LRC, Data Governance, and BUs to manage sharing/reuse constraints.
Ensure the practical advisor/client data foundation is captured and maintained by closing gaps in collection and reuse (platform-by-platform capture, cross-referencing across products, and disciplined retention of learnings).
Prioritize engineering work to build and harden priority data assets and accelerate adoption of enterprise platforms—focused on reusable, high-value data products.
Define and execute AI-readiness standards (including minimum semantic standards) and the operating model to deliver AI-ready datasets at scale (governance, quality, ownership, stewardship).
Drive pragmatic AI-ready roadmaps by prioritizing domains and use cases, establishing repeatable curation patterns for language-based systems, and increasing reuse of curated datasets vs. repeated one-off curation.