Quantitative Trading & Research - Data Product Owner/manager - Credit - Executive Director

JPMorgan Chase JPMorgan Chase · Banking · LONDON, LONDON, United Kingdom · Commercial & Investment Bank

This role is for a Data Product Owner/Manager in Quantitative Trading & Research for the Credit team at JPMorgan Chase. The primary focus is on defining, developing, and delivering data products, ensuring data quality, and managing metadata. The role involves collaborating with traders and technical teams to translate requirements, prioritize features, and maintain service level agreements for data products. It requires strong experience in financial engineering, data analysis, and programming skills, particularly in Python and database management, within the context of financial markets.

What you'd actually do

  1. Collaborate closely with Quant Research and Technology on Financial Data Products design and strategy, delivering business value with Data to Credit Trading business.
  2. Act as the business’s official Data Producer for delivering domain-specific Data Products.
  3. Define data attribute requirements and map data lineage for each Data Product.
  4. Perform testing and validation of technical implementations and sign-off.
  5. Register metadata in the catalog and ensure its evergreen status.

Skills

Required

  • financial engineering
  • data analysis in Bonds, Loans and other Credit products
  • Markets data or financial services experience
  • Python
  • data querying languages
  • relational databases
  • NoSQL databases
  • analyzing complex data sets
  • AWS, Azure, or GCP
  • communication skills
  • attention to detail
  • translate technical concepts for Sales & Trading colleagues
  • work collaboratively across multiple teams

Nice to have

  • Master’s or higher degree in Math, Physics, Computer Science, Engineering, Data Engineering, or related field
  • financial market data
  • financial data platforms
  • end-user of data feeds
  • Data Product Owner/Manager

What the JD emphasized

  • rigorous testing and validation
  • data quality metrics
  • data quality issues
  • data quality standards
  • data management
  • financial engineering
  • data analysis
  • Markets data
  • financial services
  • data querying languages
  • relational and NoSQL databases
  • analyzing complex data sets
  • AWS, Azure, or GCP
  • technical concepts
  • collaboratively across multiple teams