Director of Software Engineering - Data & AI

Your opportunity to make a meaningful impact and shape the future of Merchant Services data and reporting is waiting for you. Join us in building resilient, scalable solutions that enable better decisions for merchants and internal business partners around the world.
As a Director of Software Engineering at JPMorganChase within Commercial & Investment Bank’s Merchant Services Data and Reporting organization, you will lead the strategy, engineering, and operation of large-scale data platforms and products. The organization processes approximately 600 million transactions each day on a Snowflake-centered data platform and delivers critical solutions to external merchants and internal teams across Finance, Risk, Operations, Sales, and Marketing. This role requires an execution-focused technology leader who can translate business priorities into measurable outcomes, operate complex data ecosystems with disciplined controls, maintain production stability, and scale the platform as Merchant Services expands into markets outside the United States.
Job Responsibilities
Set and execute the technology strategy for Merchant Services Data and Reporting platforms, with accountability for measurable business outcomes, delivery commitments, platform health, and long-term scalability.
Lead the engineering and operation of Snowflake-centered data platforms that process approximately 600 million transactions per day, ensuring performance, availability, data integrity, security, and cost efficiency.
Set and scale multi-department strategy for enterprise-authorized agentic AI-enabled engineering and SDLC/TLM automation to improve speed, scalability, reliability, and cost-to-serve, with appropriate controls for validation, security, resiliency, traceability, and reuse.
Apply deep knowledge of the software development lifecycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to increase engineering effectiveness and cross-domain reuse.
Deliver trusted data and reporting solutions for external merchants and internal partners across Finance, Risk, Operations, Sales, and Marketing.
Enable data products, applications, and models for secure and governed consumption by AI agents and advanced analytics use cases.
Drive disciplined execution across complex, multi-year initiatives by establishing clear priorities, accountable owners, delivery milestones, risk mitigation plans, and transparent performance measures.
Design and evolve architectures, data models, and engineering patterns that support increasing transaction volumes, new products, and expansion into markets outside the United States.
Establish and enforce operational excellence practices, including service-level objectives, observability, capacity planning, data quality controls, resilience testing, incident response, root-cause analysis, and prevention of recurrence.
Provide leadership and direction to multiple engineering teams across platforms, locations, and lines of business while building a strong culture of ownership, accountability, inclusion, and continuous improvement.
Required qualifications, capabilities, and skills
15+ years of software engineering experience with Bachelor’s or Master’s degree in Computer Science or a related technical field, including at least 5 years building and managing large-scale, business-critical, and complex distributed data platforms.
Experience leading adoption of agentic AI-enabled engineering practices (using enterprise-authorized tools within the work environment) across teams, including defining operating expectations (human-in-the-loop validation, quality gates), measuring outcomes, and ensuring secure handling of sensitive inputs/outputs.
Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency/security implications, and governance; ability to influence leaders on safe scaling patterns and reuse.
Demonstrated experience leading Snowflake-based data platforms or comparable cloud data ecosystems at enterprise scale, including workload management, performance optimization, security, resilience, and cost governance.
Proven success operating high-volume data systems and delivering complex programs with stringent availability, recovery, data quality, and control requirements through clear prioritization, dependency management, risk mitigation, and accountability across multiple teams.
Proven track record delivering complex payment, transaction processing, data, reporting, or financial systems in a global banking or similarly regulated environment.
Experience to attract, hire, develop, retain, and recognize high-performing engineering talent.
Preferred qualifications, capabilities, and skills
Strong data engineering foundations, including complex data modeling, extensible ETL/ELT patterns, streaming and batch processing, metadata management, and multimodal search techniques.
Proficiency with SQL and at least one general-purpose programming language, commonly Python or Java.
Strong understanding of distributed systems and cloud architecture, including partitioning, storage formats, compute engines, event-driven patterns, capacity planning, and failure recovery.
Demonstrated ability to simplify complex technical topics for executives and connect engineering investments to merchant, customer, operational, risk, and financial outcomes.