Data Product Management Senior Associate

Make products production-ready with data quality, controls, documentation and adoption to support reporting, risk, and AI.

Join a core team driving Finance data-driven transformation. As a Data Product Management Senior Associate, you will help build and ship trusted Trading / Banking Book data products, partnering closely with engineering to deliver data pipelines, curated datasets, data models, and consumption-ready interfaces. You will contribute to making products production-ready through strong data quality, controls, documentation, and adoption practices that enable controllership outcomes, regulatory and management reporting, risk and controls, and advanced analytics and AI use cases. The Firmwide Financial Control (FFC) team safeguards the integrity of the Firm’s books and records and defines global target platform strategy, with the Transformation Team delivering structured data models and solutions that modernize FFC operations at scale. Job Responsibilities Drive end-to-end delivery of data product increments by translating user needs into well-defined scope, user stories, and acceptance criteria spanning data quality, timeliness, completeness, reconciliation, performance, and usability. Partner with data engineers and architects to shape logical and physical data models, source-to-target mappings, transformation rules, and clear documentation of metadata, lineage, and data definitions. Contribute to build execution by supporting sprint planning, backlog refinement, and day-to-day delivery coordination, ensuring dependencies, risks, and issues are actively managed through to release. Plan and execute testing activities for data products, including data validation, reconciliation checks, regression testing, and defect triage, partnering on root-cause analysis and remediation through to closure. Help establish production readiness by supporting monitoring and observability (data quality checks, freshness, pipeline health), and by enabling reliable consumption mechanisms (curated tables/views, extracts, or APIs as applicable) with appropriate security, privacy, and access controls. Drive release and adoption readiness by producing runbooks, release notes, and data dictionaries, supporting enablement for consumers, and maintaining feedback loops to prioritize lifecycle improvements. Required qualifications, capabilities and skills 5+ years (or equivalent) in data product management / product ownership, data management/governance, financial data delivery, analytics, or related roles in financial services or another regulated industry. Strong SQL proficiency, including working with large datasets and writing complex queries (joins, aggregations, window functions) to support data validation, reconciliation, and analysis; comfort using Excel and analytics tooling. Practical knowledge of data modeling concepts (relational and dimensional), ETL/ELT patterns, and the importance of metadata and lineage in building trustworthy, reusable data products. Familiarity with scripting for data analysis or automation (for example, Python or equivalent), with ability to apply it to validation, profiling, and repeatable checks, consistent with a Senior Associate role. Experience working in Agile delivery with a technical lens, including writing stories for data features, defining measurable acceptance criteria, collaborating closely with engineers, and supporting sprint demos/reviews with outcomes tied to quality and delivery. Working knowledge of modern delivery practices for data products, including version control, ticketing/Agile tools, and concept related to data pipelines and transformations. Preferred qualifications, capabilities and skills Exposure to Finance, Financial Control, Risk, or Treasury domains (for example, General Ledger, reference data, or finance/risk data warehouse environments). Working knowledge of modern cloud and lakehouse-style platforms and distributed processing concepts (for example, AWS, Databricks, Snowflake), including scalability, reliability, and cost-awareness fundamentals. Exposure to data governance and controls practices, including data quality rule frameworks, cataloging and metadata management, usage classification, access controls, and evidence capture for controls and reporting. Exposure to enabling data readiness for advanced analytics and AI use cases (for example, anomaly detection support), including producing feature-quality datasets and consistent, well-documented signals suitable for downstream consumption. Experience enabling self-service consumption of data products (for example, semantic layers, dashboards, self-service analytics, or APIs), partnering effectively with product, engineering, and design to improve usability and adoption.