Bring your expertise to JPMorgan Chase. As part of Risk Management and Compliance, you are at the center of keeping JPMorgan Chase strong and resilient. You help the firm grow its business in a responsible way by anticipating new and emerging risks, and using your expert judgement to solve real-world challenges that impact our company, customers and communities. Our culture in Risk Management and Compliance is all about thinking outside the box, challenging the status quo and striving to be best-in-class.
Job Summary
As a Credit Risk Data Architect Senior Associate in the Commercial & Investment Bank Credit Risk Data Enablement Team, you will work with data product owners for our Global Banking data infrastructure by driving the evolution of our data management, architecture, and analytics best practices. You will drive our shift to a data mesh architecture, support advanced AI/ML initiatives, and steer enterprise-wide data programs, while ensuring compliance, operational excellence, and strategic innovation across data products. You will be required to be a proactive problem solver with a strong intellectual curiosity and a drive to improve our firm. Additionally, you will be required to be an excellent communicator with experience in data architecture, agile product management, and data analysis. You will thrive in a dynamic environment and will work collaboratively with diverse teams to deliver exceptional results.
Job Responsibilities
Drive the migration and integration of our cloud data infrastructure into a modern data mesh framework, collaborating with internal partners to document data integrations, SLAs, and data dictionaries (e.g., using Databricks Unity Catalog).Apply a deep understanding of data lifecycle management, metadata management, and data lineage to help execute on end-to-end enterprise data governance.Design ETL pipelines to automate the extraction, integration, and federation of data, complete with automated validation to maintain high data accuracyWork with data scientists leveraging machine learning and Gen AI techniques to deliver predictive analytics, anomaly detection, and actionable business insights.Collaborate on developing data models that promote data-driven decision-making across Risk, Banking, and Finance functions.Guide the development of conceptual, logical, and physical data models to ensure alignment with enterprise data strategies.Act as a liaison with partners, fostering relationships that drive continuous improvement and platform innovation.Oversee enterprise-level data initiatives through robust program management, managing requirements and development timelines as part of digital transformation efforts.Help steer agile projects from conception through execution, ensuring seamless transitions and lasting improvements.Champion new data-driven initiatives and demonstrate adaptability, critical thinking, and strategic vision in rapidly evolving business environments.Required Qualifications, Capabilities, and Skills
Minimum 3+ years of progressive experience in data architecture, data engineering, technology consulting, or product management.Proven expertise in data integration, ETL development, and data mesh architecture within global financial environments.Strong technical proficiency in SQL, Python, and modern data visualization tools (e.g., Qlik, Tableau, or Power BI), with hands-on experience using cloud platforms (DataBricks, Snowflake, AWS Redshift).Demonstrated mastery in enterprise data management—including data lifecycle, metadata, and lineage practices.Solid experience with data modeling (conceptual, logical, and physical) and familiarity with architecture frameworks.Excellent project and portfolio management skills, with a track record of engaging in transformation initiatives.Exceptional communication skills, effective in translating complex technical concepts for diverse, global audiences.Preferred Qualifications, Capabilities, and Skills
Experience with advanced analytical tools (e.g., Python, Alteryx) Prior exposure to financial institutions and regulatory reporting requirements.Relevant certifications, such as AWS Certified Cloud Practitioner or Databricks Certified Data AnalystFormal education in Computer Science, Information Systems, or a related field; graduate studies are a plus.