Strategic Data Provisioning Specialist at Chief Data & Analytics Office (CDAO)

The Chief Data & Analytics Office (CDAO) at JPMorgan Chase Asset and Wealth Management (AWM) is responsible for accelerating AWMs 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 experiencein data science analytics data engineering or data management within financial services
• Deep subject matter expertisein wealth and asset management covering customer account position transaction and/or reference data domains
• Proven execution abilityin 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 skillsin data profiling analysis and data management using modern tools and environments (Python R SQL Spark cloud platforms)
• Experience withdata quality frameworks including profiling rule development issue remediation and preventative controls
  

Preferred Qualifications Capabilities and Skills

• Strong proficiency indata science and analytics tools: Python R SQL Spark and cloud data platforms (AWS Azure GCP)
• Experience withdata visualization and reporting tools(e.g. Tableau Power BI) to deliver executive dashboards and performance metrics
• Hands-on experience withdata lineage toolsand techniques including graph databases and metadata management platforms
• Knowledge ofdata governance frameworks data quality dimensions and regulatory requirements (e.g. BCBS 239 GDPR)
• Experience withAI/ML technologiesand their application to data management challenges (e.g. automated data profiling metadata enrichment)
• Understanding ofagile and product management methodologiesand experience working in agile teams
• Strong judgmentwith 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