Director of Software Engineering

If you are a software engineering leader ready to take the reins and drive impact, we've got an opportunity just for you.

As a Director of Software Engineering at JPMorganChase within the within the Commercial and Investment Bank, you will own a dual mandate across platform/build (Snowflake enablement, reusable capabilities, reliability, security/controls) and business-facing value delivery (merchant and sales insights, risk and loss reduction, operational efficiency). You will role partner closely with Product, Sales, Operations, Architecture, and Risk/Controls to deliver scalable capabilities and outcomes the field can use immediately. This is a hands-on technology leadership role with a high bar for engineering excellence, including code quality, secure-by-design development, automated testing, CI/CD discipline, and operational readiness.

Job Responsibilities

• Defines reference architectures and reusable components with engineering and platform teams (e.g., payments-scale aggregation, merchant entity resolution). Drives delivery excellence through agile practices, milestone execution, and rigorous dependency management.
• Operates a production-grade SDLC for data/AI products: CI/CD, automated testing, observability, runbooks, incident response, and rollback.
• Delivers analytics products end-to-end (dashboards and KPIs, self-serve analytics, predictive and decision support) with adoption and lifecycle ownership.
• Drive timely delivery through strong execution mechanics (milestones, dependency management, CI/CD automation, release discipline).
• Standardize critical metric definitions to reduce drift and reconciliation (e.g., TPV, authorization rate, losses, disputes and chargebacks).
• Run governance forums and issue and remediation processes aligned to delivery priorities. Enables sales and business teams with timely, trusted insights that drive targeting, retention, and portfolio actions.
• Manages budget and capacity planning, partner engagement as needed, and ROI and value tracking tied to outcomes.
• Sets direction and governance for agentic AI-enabled engineering and SDLC/TLM automation within a technical area to drive measurable improvements in speed, quality, and operational outcomes (e.g., AI-orchestrated delivery workflows, release readiness controls, automated test modernization, and incident triage acceleration), while establishing guardrails for validation, security, resiliency, traceability, and reuse across teams.
• Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation and support capacity unlock initiatives at scale

Required qualifications, capabilities, and skills

• Track record delivering enterprise analytics and AI solutions end-to-end, from intake and roadmap to production, adoption, and ongoing operations.
• Demonstrated engineering leadership with strong SDLC discipline (code reviews, automated testing, CI/CD, release governance).
• Ownership of non-functional requirements for business-critical platforms (availability, resiliency, performance, observability, security).
• Strong understanding of modern data platforms and governance (data products, metadata and lineage, data quality, access controls).
• Business value delivered (revenue growth, cost reduction, risk and loss reduction). Adoption and satisfaction of analytics, AI, and self-serve capabilities.
• Delivery and reliability (time-to-market, availability and SLO attainment, incident rate and MTTR). Data trust and governance (quality SLAs, certified datasets, lineage and metadata coverage, access turnaround).
• Agent performance and audit readiness (task success rate, evaluation results, incident rate, control effectiveness).
• Executive stakeholder management across Technology, Product, Sales, Operations, and Risk/Controls, with the ability to translate strategy into measurable outcomes.
• 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.

Preferred qualifications, capabilities, and skills

• Experience building and operating LLM and agentic systems with evaluation, safety controls, monitoring, and human oversight.
• Experience operating high-throughput, low-latency, 24x7 platforms (payments strongly preferred).
• Experience in regulated environments and merchant payments familiarity (authorization performance, disputes and chargebacks, fraud and loss, onboarding and KYC, servicing workflows).