Associate Capital Data Product Owner

Build data products that directly shape how the firm measures and manages capital. You’ll turn complex business needs into trusted, well-governed datasets and data services that power critical reporting, analytics, and decision-making.

As an Associate Capital Data Product Owner within Capital Data Strategy team, you will help shape and execute the Capital Data Strategy by owning delivery for priority Capital data products and enabling capabilities under the Data Product 2.0 program. You will translate capital measurement and reporting outcomes (e.g. RWA, capital ratios, stress testing inputs, capital attribution/drivers) into well-defined product increments - covering ingestion, persistence, transformations, controls, and distribution - while raising the bar on data quality, lineage, and operational reliability.

You will work in an agile model, partner closely with engineering and Capital stakeholders, and develop the product management muscle to potentially step into a Data Product Manager role for a defined subset of Capital Data Products.

Job Responsibilities

• Own end-to-end delivery for assigned Capital Data Products end-to-end (or key slices), managing backlog across sourcing, ingestion, validation, persistence, transformation, and distribution.
• Translate Capital business outcomes into epics/user stories with clear acceptance criteria tied to consumer usability, controls, and data quality.
• Drive Data Product 2.0 adoption for your scope: standard schemas, metadata completeness, lineage, certification, and documented SLAs/SLOs.
• Track to OKRs/KPIs for data products to evolve/mature overall usage, reusage, quality and AI readiness.
• Implement consistent product constructs: product definitions, domain glossary alignment, ownership/RACI, quality rules, and operational readiness.
• Partner with platform teams to onboard products onto shared capabilities (e.g., catalog/metadata, DQ tooling, observability, entitlement patterns).
• Review and contribute to conceptual/logical data models (entities, attributes, relationships, hierarchies) ensuring they support capital calculations and downstream reuse.
• Define and track data quality rules and thresholds (completeness, validity, timeliness, reconciliation, duplicates), including monitoring and alerting requirements.
• Ensure requirements address PII handling where applicable, entitlements/access controls, retention, auditability, and traceability expected for capital-related data.
• Lead consumer enablement and delivery execution: support new feature discovery by gathering and prioritising requirements, support end-to-end use AI-assisted SDLC practices with strong human validation and a controls mindset.
• Define distribution patterns and contracts (e.g., curated datasets, APIs, batch extracts): selection logic, formats, schedules, reconciliation, reruns/backfills, and SLAs.

Required qualifications, capabilities, and skills

• few years of experience in Product Owner, Business Analyst, Data Analyst, or Technology Delivery roles.
• Strong requirements writing and backlog management skills; comfortable in agile delivery.
• Solid understanding of data product concepts (data contracts, consumers, ownership, quality, observability, lifecycle).
• Ability to read and contribute to logical/conceptual data models and shared domain definitions.
• Strong SQL/data reasoning skills to validate issues, investigate anomalies, and confirm fixes.
• Clear communication and stakeholder management across engineering + business + data governance partners.
• High ownership mindset: proactive, organized, and able to drive work through ambiguity.

Preferred qualifications, capabilities, and skills

• Any exposure to bank capital concepts (e.g., RWA, capital ratios, stress testing inputs, capital reporting/attribution, regulatory reporting).
• Experience with Data Product -style enablement: metadata, lineage, DQ rules, certification, and operating model.
• Familiarity with data engineering patterns (batch + near-real-time), data lake/warehouse concepts, and distributed systems fundamentals.
• Experience defining operational SLAs/SLOs, reconciliation controls, and runbook-style production readiness.
• Comfort presenting updates to senior stakeholders; can land crisp “so what / now what” narratives.