Director of Software Engineering - Data & AI

JPMorganChase•Plano, TX

About The Position

As a Director of Software Engineering in JPMorganChase’s Commercial & Investment Bank 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 platform and delivers critical solutions to external merchants and internal partners across Finance, Risk, Operations, Sales, and Marketing. This execution-focused technology leadership role translates business priorities into measurable outcomes, operates complex data ecosystems with disciplined controls, maintains production stability, and scales the platform as Merchant Services expands into markets outside the United States. You will lead multiple engineering teams across platforms, locations, and lines of business, and scale enterprise-authorized agentic AI-enabled engineering and SDLC/TLM automation with appropriate controls.

Requirements

15+ years of software engineering experience and a Bachelor’s or Master’s degree in Computer Science or a related technical field.
At least 5 years building and managing large-scale, business-critical, complex distributed data platforms.
Experience leading adoption of agentic AI-enabled engineering practices using enterprise-authorized tools, including defining human-in-the-loop validation and quality gates, measuring outcomes, and securely handling sensitive inputs and outputs.
Strong understanding of responsible AI use and control expectations in engineering workflows, including data sensitivity, resiliency and security implications, and governance; ability to influence leaders on safe scaling 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, including prioritization, dependency management, risk mitigation, and cross-team accountability.
Proven track record delivering complex payment, transaction processing, data, reporting, or financial systems in a global banking or similarly regulated environment.
Experience attracting, hiring, developing, retaining, and recognizing high-performing engineering talent.

Nice To Haves

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.
Ability to simplify complex technical topics for executives and connect engineering investments to merchant, customer, operational, risk, and financial outcomes.

Responsibilities

Set and execute technology strategy for Merchant Services Data and Reporting platforms, with accountability for business outcomes, delivery commitments, platform health, and long-term scalability.
Lead engineering and operation of Snowflake-centered data platforms processing 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, with controls for validation, security, resiliency, traceability, and reuse.
Apply knowledge of the software development lifecycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve 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 complex, multi-year initiatives through clear priorities, accountable owners, delivery milestones, risk mitigation plans, and transparent performance measures.
Design and evolve architectures, data models, and engineering patterns to 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.
Lead multiple engineering teams across platforms, locations, and lines of business while building a culture of ownership, accountability, inclusion, and continuous improvement.
Attract, hire, develop, retain, and recognize high-performing engineering talent.