Manager, Data & Context Governance

BetaNXT Inc•New York, NY

1d•$125,000 - $150,000

About The Position

BetaNXT is expanding its data governance function to meet the demands of a rapidly evolving data and AI landscape. We are seeking a Manager, Data & Context Governance to build and lead a team of data stewards and integration specialists responsible for both traditional data governance and a new, equally critical discipline: context governance — ensuring the information, definitions, and business context that power our AI and LLM-driven systems and products are accurate, complete, and trustworthy. This Manager will hire, lead, and develop a dedicated team, with each team member aligned to a functional business domain, owning governance outcomes for both structured data assets and the context that drives AI-generated outputs. Beyond installing sound governance practice, this Manager is expected to operate as a strategic partner and execution driver across BetaNXT’s active data and AI initiatives — working hands-on with project teams to ensure governance is built into delivery from the start, not bolted on afterward.

Requirements

3+ years in a leadership role within data governance, with direct people-management experience.
10+ years in enterprise data teams, with substantial experience in financial services, banking, or wealth management.
Strong understanding of data governance principles, metadata management, data quality, and data stewardship.
Familiarity with data governance tools and technologies (e.g., Data.World, Informatica, Collibra, Alation).
Working knowledge of how LLM/AI systems consume context (retrieval sources, knowledge bases, prompt/agent design) and how gaps in context affect output quality — prior hands-on AI/LLM governance experience is a strong plus but not required.
Proven ability to build and lead a team from the ground up, including defining new roles and operating models.
Excellent understanding of regulatory requirements related to data privacy and security.
Exceptional leadership and communication skills, with the ability to influence across all organizational levels and act as a strategic partner to project teams, not solely a governance-office function.
Strong documentation skills to maintain clear records of governance processes, policies, and decisions.

Nice To Haves

prior hands-on AI/LLM governance experience is a strong plus but not required.

Responsibilities

Build, lead, and develop a team of data stewards and integration specialists, each aligned to a functional business domain (e.g., Trading, Accounts, Positions, Pricing) as their primary partner area.
Define role clarity, workload distribution, and performance expectations for team members covering both traditional data governance and context/AI governance responsibilities.
Serve as the escalation point and quality bar-setter for team output across all domains, owning the end-to-end result even where day-to-day work is delegated.
Develop and execute a data governance strategy that spans structured data assets and the broader context assets (definitions, business rules, taxonomies, knowledge sources) that feed AI and LLM systems and products.
Partner directly with product, architecture, and business stakeholders across data and AI initiatives (including the Data Integration Hub) to embed governance requirements into project planning and execution, rather than governing after the fact.
Own strategic direction for the data governance tooling, ensuring it is configured and maintained to achieve intended data governance and context governance outcomes (module ownership sits with this Manager; platform architecture is owned separately within the team).
Establish governance practices for the context that drives AI/LLM outputs, including: Curating and maintaining the knowledge sources, definitions, and business rules that feed AI/LLM systems and products. Overseeing validation and quality review of AI-generated outputs against approved source context. Establishing governance standards for prompt and agent-instruction design where it affects output accuracy and consistency.
Define what “good context” looks like for each business domain and ensure the team applies consistent standards across domains.
Design and operationalize a dedicated stewardship model, with clear roles, responsibilities, and processes across both data and context governance.
Define domain-alignment structures that connect each team member to the functional business area(s) they support.
Define and oversee data quality standards, processes, and metrics for structured data assets.
Ensure the completeness and accuracy of the data catalog, data dictionary, and related metadata artifacts.
Ensure governance practices support compliance with relevant data protection and industry regulations.
Establish and report on KPIs spanning both governance disciplines, including: Traditional metrics: catalog/metadata completeness, team SLA adherence, data quality issue resolution. AI-output metrics: accuracy/groundedness of AI-driven outputs, reduction in escalations or hallucination-related incidents tied to context gaps.
Provide regular reporting to leadership on governance program health and business impact.
Develop training and enablement to raise data and context literacy across the organization.
Lead change management to integrate governance practices into existing workflows.
Continuously evaluate and refine governance processes as business needs and AI capabilities evolve.