Sr. Director of Software Engineering

JPMorgan Chase & Co.•Jersey City, NJ

5h•$232,750 - $325,000

About The Position

As a Sr. Director of Software Engineering at JPMorgan Chase within the Audit Tech team in Corporate Technology, you drive innovation, mentor tech talent, and translate complex data into powerful insights. You'll oversee our tech ecosystem spanning internal and public clouds, optimizing for peak performance. Utilize your in-depth knowledge of software, applications, technical processes, and product management to drive multiple complex projects and initiatives, while serving as a primary decision maker for your teams and be a driver of innovation and solution delivery. If you're passionate about Audit tech, thrive on challenges, and excel at translating business requirements into elegant technical solutions, we want you! Join us to shape the future of audit software engineering.

Requirements

Formal training or certification on software engineering concepts and 10+ years applied experience. In addition, 5+ years of experience leading technologists to manage, anticipate and solve complex technical items within your domain of expertise
Strong background in financial technology and understanding of data-intensive applications
Expertise in Java programming and familiarity with Python and Angular
Experience with cloud technologies, particularly AWS
Proficiency in Oracle database management and optimization
Proven track record of successfully leading large-scale software development projects
Strong knowledge of Agile methodologies and DevOps practices
Demonstrated ability to work effectively with business stakeholders and technical teams
Experience in managing budgets and resources for software development projects
Experience leading multi-organization adoption of agentic AI-enabled engineering operating models (using enterprise-authorized tools within the work environment), including defining governance (human-in-the-loop decisioning, quality gates), measurement frameworks, and secure handling of sensitive inputs/outputs across teams.
Deep understanding of responsible AI risk, controls, and resiliency/security expectations at scale, with demonstrated ability to advise senior leaders on safe adoption, portfolio governance, and reuse-first strategies.

Nice To Haves

The ideal candidate would have deep working knowledge of public cloud, native-cloud applications, microservices, databases, data lakes.
Ensure all software development adheres to financial industry regulations and security standards

Responsibilities

Lead and manage multiple software engineering teams, fostering a culture of innovation and continuous improvement
Define strategy, vision, alignment to architecture standards and data strategy , use of AI to advance envisioned transformation and technology selection.
Partner with senior stakeholders, business users and product leadership, tech leadership across Audit & Corporate Technology to build strong partnerships
Collaborate with business users to understand requirements and translate them into technical solutions
Oversee the full software development lifecycle for concurrent projects, ensuring timely delivery within budget
Drive the adoption of best practices in software development, including Agile methodologies and DevOps practices
Mentor, coach, and develop software engineers and development leads to enhance their technical and leadership skills
Establish and implement software development standards, methodologies, and quality assurance processes
Manage resource allocation, budgets, and strategic planning for the software engineering department
Sets and scales multi-department strategy for agentic AI-enabled engineering and SDLC/TLM automation (using enterprise-authorized tools within the work environment) to drive firmwide objectives (speed, scalability, reliability, and cost-to-serve), including portfolio-level standards for AI-orchestrated delivery workflows, release governance, automated test modernization, resilience engineering, and incident response acceleration; establishes guardrails for validation, security, resiliency, traceability, and reuse.
Applies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to drive cross-domain reuse and measurable capacity unlock outcomes across departments.