J.P. Morgan’s Corporate & Investment Bank (CIB) is a global leader across banking, markets and investor services. The world’s most important corporations, governments and institutions entrust us with their business in more than 100 countries. With $18 trillion of assets under custody and $393 billion in deposits, the Corporate & Investment Bank provides strategic advice, raises capital, manages risk and extends liquidity in markets around the world.

 Background:

Payments Operation (PO) is part of the Digital & Platform Services organization in the Corporate & Investment Bank. PO supports Payments, Receivables and Trade Finance Products and acts as the payment hub for JPMorgan Chase across the firm’s business lines. PO consists of 5,500 employees across 60 sites and supports 142k corporations, financial institutions, governments, and municipalities domiciled in over 180 countries. On average, PO processes 30.1MM transactions daily across 122 currencies valued at $3.2T. 

 Payments Operations and the Payments industry are undergoing significant amounts of change and disruption – industry, technology, and organizational. It is critical to develop and execute on a strategy that will enhance the organization’s business and operational model and position it for continued success. 

Role Description:

We are seeking an experienced Data Domain Architect to join the Strategy, Innovation & Digitization Data team within the Payments Operations organization. The role will work within a team that provides data architecture leadership and strategy, and solutions data engineering use cases focused on data analytics, metrics and insights across Payments Operations.  This team is a service provider to the various Payments Operations teams and to be successful, the candidate will need to closely collaborate with product, analytics and technology teams.

This role is ideal for a highly motivated individual with a strong passion for data, problem solving, and domain knowledge as well as strong interpersonal and communication skills.  

Responsibilities:

Facilitate requirements definition, design, testing, and implementation of new data delivery and analytic capabilities that address specific business needs for Payments Operations. Define data (metadata), identify systems of record and authoritative sources, create data quality rules, create data flow diagrams, and apply firm-wide standards and controls. Create conceptual and logical models to describe a particular domain of data and use these models to inform the physical design of data-related projects; consult enterprise and industry models. Profile, wrangle, and prepare data from diverse sources to support analytical efforts; write data transformation logic in languages such as Python, SAS, or Scala. Conduct business process analysis and identify data needed to support the processes.  Determine whether the requested data is fit for use within a given process. Conduct research and development with emerging technologies, determine their applicability to business use cases, document & communicate their recommended use in the firm. Work with Tech, Product and CIB data partners to research, define, and implement use cases Work closely with data analytics and data product owner staff across our team to understand requirements and partner to optimize solutions and develop / foster new ideas

Qualifications:

3+ years of relevant work experience as a software developer, data/ML engineer, data scientist, business intelligence engineer Minimum of Bachelor’s degree in Computer Science/Financial Engineering, MIS, Mathematics, Statistics or other quantitative subject Analytical thinking and problem-solving skills coupled with ability to understand business requirements and to communicate complex information effectively to broad audiences Experience or knowledge in at least one data technology; i.e., data warehousing, ETL, data quality concepts, Business Intelligence tools and analytical tools,  unstructured data, machine learning, etc. Experience using common relational database systems; i.e., Teradata and Oracle with strong SQL skills Cloud platform knowledge; hands on experience with Databricks or Snowflake Hands-on experience with data modeling Knowledge of the SQL, SAS or Scala, and Python languages Knowledge of Advanced Statistics  Domain Expertise