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Lead Data Management at JPMorgan Chase, driving strategy, governance, and AI/ML-enabled data products.
Collaborate with cross-functional teams, shape data culture, and innovate with cutting‑edge technologies.
Overview
Join the Chief Administrative Office – Chief Data & Analytics Office and help shape the future of data management at JPMorganChase. As a Data Owner Lead, you will drive impactful data strategies and foster a data-driven culture. This role offers opportunities for career growth, skill development, and collaboration with industry-leading experts. Be part of a team that leverages AI and machine learning to support the firm’s commercial goals. In addition to strong governance execution, this role emphasizes hands‑on data product delivery—bringing data together from multiple sources, shaping it into high‑quality, reusable data products, and enabling broad consumer populations and advanced analytical solutions.
As a Data Owner Lead within the Chief Data & Analytics Office, you will execute data strategies that align with business operations and strategic objectives. You will collaborate with cross‑functional teams to ensure data is understood, fit for purpose, and well‑governed. You will also partner with Technology and Analytics to define and maintain data products, including curation, semantic consistency, documentation, and lifecycle management. In this role, you will help drive organizational growth and competitive advantage by supporting a culture of data‑driven decision‑making.
You will work closely with partners across Business, Technology, Analytics, Operations, and Risk and Control functions. Your leadership will ensure data quality, integrity, and security, while supporting innovation and compliance with data governance standards. You will partner with information architecture and data platform teams to ensure data products are scalable, discoverable, and fit for enterprise use.
Job Responsibilities
- Implement strategic plans to deliver data solutions and data products that support business operations and strategic objectives.
- Manage project execution, mitigating risks and inefficiencies.
- Collaborate with partners to document and classify critical data with metadata. Ensure metadata is actionable and supports discovery and reuse (business definitions, technical metadata, lineage, quality rules/thresholds, and product documentation).
- Provide subject matter expertise on data content and usage within the business and associated product areas.
- Develop relationships with data providers and consumers across multiple functions. Engage broad‑based consumer populations (operations, reporting, analytics, data science) to understand usage patterns and improve usability of curated datasets and data products.
- Document requirements for data sourcing, content, and quality. Define requirements for integrating multiple upstream sources (including mapping, transformation logic, and reconciliation) and for building curated datasets that support analytics and reporting use cases.
- Develop processes to identify and mitigate data risks throughout the data lifecycle, including data protection, privacy, retention, destruction, storage, use, and quality. Ensure controls are operationalized within pipelines and data products (e.g., automated quality checks, monitoring/alerting, and auditable evidence).
- Support data analytics by governing data integration into analytics platforms, including designing fit‑for‑purpose curated datasets and feature‑ready data products for advanced analytics/ML use cases.
- Communicate and resolve data issues, maintaining data integrity; Support data issue triage and root‑cause analysis with business and technology partners; drive remediation and preventative controls to reduce recurrence.
- Leverage AI/ML and LLM tooling to automate governance and data management activities (e.g., metadata drafting/classification, control evidence generation, quality rule suggestions, lineage/documentation assistance), including prompt creation and evaluation; contribute to agent‑based workflow designs where appropriate.
Required Qualifications, Capabilities, and Skills
- Bachelor’s degree in Data Science, Computer Science, Information Systems, Data Analytics, or equivalent professional experience.
- Five years of experience in data management, data governance, or risk management/analytics. Demonstrated experience delivering or partnering closely on delivery of data products or curated datasets that integrate multiple sources for enterprise consumption.
- Proven leadership track record with the ability to manage delivery timelines.
- In‑depth understanding of data management principles and governance frameworks. Working knowledge of the data development lifecycle and familiarity with the operating model for data products (build, release, run, change).
- Excellent analytical and problem‑solving skills.
- Strong communication skills for technical and non‑technical stakeholders.
- Strong leadership skills with experience in managing cross‑functional teams.
- Proven ability to build relationships with key stakeholders and manage large‑scale data projects; Ability to translate consumer needs into clear data requirements, acceptance criteria, and measurable outcomes (quality, usability, adoption).
Preferred Qualifications, Capabilities, and Skills
- Experience with cloud‑based data platforms such as AWS, Azure, or Google Cloud. Experience with data lake/lakehouse concepts and information architecture practices.
- Familiarity with advanced analytics, machine learning, or AI applications; Hands‑on experience leveraging LLMs for productivity and governance automation (prompt engineering patterns, agent/tooling concepts, evaluation/monitoring of outputs).
- Knowledge of query or analytical programming languages. (e.g., SQL; Oracle a plus) and comfort partnering with engineering on ingestion/transformation patterns.
- Experience in leading digital transformation initiatives leveraging data.
- Experience in data product management; Experience establishing operating processes for data products (documentation standards, quality SLAs/SLOs, monitoring/alerting, and lifecycle/deprecation).