Join our team to build and scale a mission-critical data product. You’ll own the product vision and execution for many efforts, including the data services that power client reporting, operational workflows, and investment processes—while pushing the frontier on AI-enabled automation, Agentic AI readiness, and modern data quality engineering.
Job Summary
As a data owner on a mission-critical data product, you’ll own the product vision and execution for many efforts, including the data services that power client reporting, operational workflows, and investment processes—while pushing the frontier on AI-enabled automation, Agentic AI readiness, and modern data quality engineering.This role is for a builder who thrives in ambiguity, moves with agility and sharp judgment, and turns complex, high-stakes data problems into durable products that teams actually adopt. If you want a long runway, real impact, and a front-row seat to how AI reshapes enterprise data, this is your seat
Job responsibilities
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Own delivery end-to-end for a critical data domain—intake through outcomes—ensuring delivery aligns to business priorities and measurable client/user value.
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Run the full data ownership agenda: governance and controls, domain boundaries, downstream consumption from systems-of-record, retention & destruction and AI readiness.
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Translate complex needs into crisp product direction: define problem statements, use cases, success metrics, user journeys, and acceptance criteria that enable delivery teams to move quickly without losing rigor.
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Design and drive a modernization roadmap spanning data platform evolution, quality tooling, metadata/lineage enablement, and scalable operational processes.
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Identify and implement AI opportunities increasing straight-through processing, and improving data quality—while ensuring solutions are safe, explainable, and governable.
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Drive execution with cross-functional partners across Business, Operations, Technology, and Controls—removing blockers, establishing operating models, clarifying decisions, and accelerating delivery.
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Manage dependencies and sequencing across teams, platforms, and release trains to ensure predictable, high-quality outcomes.
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Measure what matters: establish and maintain delivery and outcome metrics (quality, timeliness, adoption, STP lift, defect reduction, cycle time) and use them to refine the roadmap.
Required qualifications, capabilities, and skills
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Proven track record in data management: system-of-record, lineage, metadata, data quality, controls, stewardship, and lifecycle management.
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At least 10 years of Financial Services experience, with data, technology and operations experience blended with knowledge of Asset& Wealth Management data concepts
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Delivered in agile environments with real backlog discipline, pragmatic scope control, and outcome-based execution
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Comfortable with SQL and/or Python, navigating large datasets to validate hypotheses, investigate issues, and partner credibly with engineering and analytics
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Practical fluency with AI and Agentic AI for data products: automated reconciliations, anomaly detection, data-issue triage, metadata enrichment, exception handling, and workflow orchestration
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Able to evaluate AI honestly — value, risk, scalability, control points, operational readiness — and turn experimentation into production outcomes
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Influence stakeholders at every level, aligning diverse groups around clear priorities and standards
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Excellent written and verbal communication, including materials sharp enough for governance forums and senior leadership.
Preferred qualifications, capabilities, and skills
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Hands-on with foundational AI tooling — VS Code, GitHub Copilot — and the modern developer environment
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Hands-on with data-quality tooling, metadata platforms, lineage frameworks, and modern data platforms (lakehouse/warehouse patterns, distributed processing, APIs/data services)