The Chief Data & Analytics Office (CDAO) at JPMorgan Chase Asset and Wealth Management (AWM) is responsible for accelerating AWM's data and analytics journey. The Strategic Data Provisioning (SDP) team plays a critical role in modelling behaviours to drive adoption, manage dependencies, align resources, foster innovation, and demonstrate value across the data lifecycle.
We are seeking an execution-focused Strategic Data Provisioning Specialist to deliver on four primary service areas: provisioning new and differentiated data, tracing and uplifting lineage, resolving data quality issues, and uplifting existing data. This role requires a unique combination of deep technical expertise, strategic thinking, and collaborative leadership to make data available for AI/analytics, provide transparency into data flows, embed preventative controls, and enrich metadata to accelerate adoption.
Job Responsibilities
- Provision New/Different Data - Make data available for AI and analytics initiatives, working closely with use case owners to define requirements and manage product dependencies Provide transparency and visibility into bottlenecks and progress in making AI-ready data available for innovation, Collaborate with business, technology, and operations partners to understand data requests and accelerate provisioning through deployment of "AI for Data" and Drive executive visibility of progress in making critical data sources available, including performance metrics and adoption tracking Support agile product routines to oversee cross-product data dependencies and prioritize delivery,
- Trace & Uplift Lineage - Identify the lineage and provenance of critical data assets to support governance, regulatory, and business requirements, Embed evergreen controls on data flows to improve safety and meet regulatory requirements and Develop and deliver data lineage analysis and documentation that provides executive visibility on progress meeting critical SLAs (including blockers, resourcing, etc.). Uplift data flows for critical data to include controls, transparency, and traceability and Drive insight into areas of efficiency and risk through consolidation and reengineering of data flows
- Resolve Data Quality Issues - Lead data quality issue root cause analysis using deep data profiling and advanced analytics techniques, Fix the cause of identified data quality issues and embed uplifted evergreen controls on data flows to prevent future failures and Develop proactive controls to reduce the time from data quality issue identification to resolution, improving client experience Drive operational efficiency through elimination of cost of poor quality (COPQ) and Demonstrate control environment improvements and reduction in toil to achieve benefits through common tooling and frameworks
- Uplift Existing Data - Uplift the metadata (semantic layer) of existing data to make it more valuable to users and AI applications (AKA "Brownfield" data enrichment) and Support AI and Natural Language Query (NLQ) usage through enhanced data cataloguing and discoverability. Accelerate adoption of Mesh data architecture by enriching existing data assets with improved metadata, data quality scores, and lineage information Reduce consumer friction due to poor data catalogue quality and incomplete documentation and Develop and deliver data product prototypes that demonstrate the value of uplifted data assets
Required Qualifications, Capabilities, and Skills
- 7+ years of experience in data science, analytics, data engineering, or data management within financial services
- Deep subject matter expertise in wealth and asset management, covering customer, account, position, transaction, and/or reference data domains
- Proven execution ability in a matrixed and complex environment with the ability to influence people at all levels of the organization
- Experience in strategic or transformational change initiatives, including data governance, data quality, or analytics transformation programs
- Strong technical skills in data profiling, analysis, and data management using modern tools and environments (Python, R, SQL, Spark, cloud platforms)
- Experience with data quality frameworks, including profiling, rule development, issue remediation, and preventative controls
Preferred Qualifications, Capabilities, and Skills
- Strong proficiency in data science and analytics tools: Python, R, SQL, Spark, and cloud data platforms (AWS, Azure, GCP)
- Experience with data visualization and reporting tools (e.g., Tableau, Power BI) to deliver executive dashboards and performance metrics
- Hands-on experience with data lineage tools and techniques, including graph databases and metadata management platforms
- Knowledge of data governance frameworks, data quality dimensions, and regulatory requirements (e.g., BCBS 239, GDPR)
- Experience with AI/ML technologies and their application to data management challenges (e.g., automated data profiling, metadata enrichment)
- Understanding of agile and product management methodologies and experience working in agile teams
- Strong judgment with the ability to balance strategic vision with pragmatic, incremental delivery
Technical Skills
- Programming & Analysis: Python, R, SQL, PySpark
- Cloud Platforms: AWS, Azure, GCP
- Data Tools: Data profiling tools, data quality platforms, metadata management systems
- Visualization: Tableau, Power BI, or similar BI tools
- Methodologies: Agile/Scrum, DevOps, Data Ops
- Version Control: Git, GitHub, Bitbucket