What are the responsibilities and job description for the Lead Technical Program Manager - AIML Databricks position at JPMorgan Chase?
Elevate your career by steering multi-faceted technology programs, integrating innovative solutions, and driving impact across global operations.
The Chief Data & Analytics Office (CDAO) at JPMorganChase is responsible for accelerating the firm’s data and analytics journey. This includes ensuring the quality, integrity, and security of the firm’s data, as well as leveraging it to generate insights and drive decision-making. The Chief Data & Analytics office (CDAO) also develops and implements solutions that support the firm’s commercial goals by responsibly harnessing artificial intelligence and machine learning to develop new products, improve productivity, and enhance risk management.
As a Lead Technical Program Manager at JPMorganChase within Corporate AIML Data Platforms & Chief Data & Analytics (CDAO), you will lead complex, cross-functional technology programs that impact experiences across the firm, including clients, employees, and stakeholders. You will use strong analytical reasoning and adaptability to break down business, technical, and operational objectives into manageable workstreams, navigate ambiguity, and drive change. With demonstrated technical fluency, you will manage resources, budgets, and cross-functional teams to deliver innovative solutions aligned to strategic goals. Your exceptional communication and influencing skills will foster productive stakeholder relationships, ensuring alignment and effective risk management. In this pivotal role, you will also contribute to new policies and processes that shape the future of our technology landscape.
Job responsibilities
- Creates and maintains detailed project plans, timelines, and delivery schedules for assigned programs
- Manages day-to-day program execution using JIRA, Confluence, and other project management tools, ensuring accurate tracking of user stories, epics, and sprint progress
- Facilitates agile ceremonies, including sprint planning, daily standups, retrospectives, and backlog grooming, with engineering teams
- Maintains comprehensive program documentation, including status reports, risk registers, RAID logs, and dependency matrices
- Oversees engineering risks, issues, and dependencies across assigned programs
- Engages stakeholders, including customers and partners across Lines of Business (LOBs)
- Guides the selection and implementation of appropriate technologies, platforms, and software tools, leveraging technical fluency
- Champions continuous improvement by identifying process optimization opportunities, incorporating best practices, and staying abreast of emerging technologies
- Partners with Product teams to drive business outcomes, ensuring technical programs align with strategic goals
- Prepares and delivers regular status updates and presentations to stakeholders and leadership, communicating progress, risks, and blockers
Required qualifications, capabilities, and skills
- 5 years of experience (or equivalent expertise) in technical program management, leading complex technology initiatives in large organizations
- Hands-on experience with agile delivery tools (JIRA, Confluence) and planning tools (e.g., MS Project), with proven ability to maintain accurate program artifacts and metrics
- Demonstrated proficiency in technical solutioning, vendor product evaluation, vendor management, and solution implementation
- Strong analytical reasoning skills, applying critical thinking and problem-solving techniques to break down business, technical, and operational objectives
- Proven ability to lead through change, manage dependencies, and control scope in high-pressure, shifting environments
- Strong stakeholder management skills, building productive relationships and driving outcomes aligned with firm objectives
- Understanding of data platform trade-offs, including performance optimization, cost management, scalability, and operational excellence
- Deep technical understanding of modern data platform architectures, including data lakes, data warehouses, Lakehouse architectures, and distributed computing frameworks
- Experience with enterprise-scale implementations of cloud-native data platforms such as Databricks and Snowflake
- Good understanding of AWS data analytics services, including Redshift, EMR, Glue, Athena, Kinesis, Lake Formation, MSK, and S3 data lake patterns, with demonstrated experience architecting end-to-end data solutions
- Experience navigating complex data governance, security, and compliance requirements across multi-cloud and hybrid data environments at enterprise scale