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Director of Software Engineering

Columbus, OH · On-site

$244K/yr

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 ...

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How much do director of software jobs pay per year?

As of Aug 23, 2026, the average yearly pay for director of software in the United States is $243,917.00, according to ZipRecruiter salary data. Most workers in this role earn between $253,000.00 and $253,000.00 per year, depending on experience, location, and employer.

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A map of the United States highlighting the number of Director Of Software job openings by state according to ZipRecruiter. The image is accompanied by a detailed chart listing the number of Director Of Software job openings in each state, with California having the most at 2 and Hawaii the least at 0.

Director of Software Engineering

Hackajob

Columbus, OH • On-site

$244K/yr

Other

Posted yesterday

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Job description

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 Deposits team, you lead a technical area and drive impact within teams, technologies, and projects across departments. 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.

Job Responsibilities

  • Leads technology and process implementations to achieve functional technology objectives
  • Accountable for decisions that influence teams' resources, budget, tactical operations, and the execution and implementation of processes and procedures
  • 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.
  • Sets direction for reliability engineering practices within a technical area, including SLO/SLI definition, error budgets, capacity planning, and resilient design patterns aligned to business outcomes.
  • Establishes observability standards across services (metrics, logs, traces, dashboards, alerting) to accelerate detection, triage, and remediation while reducing toil and repeat incidents.
  • Drives end-to-end engineering decisions by assessing downstream impacts across integrated platforms, data flows, and dependent systems, and aligning changes to enterprise resiliency and risk expectations.
  • Influences peer leaders and senior stakeholders across the business, product, and technology teams

Required qualifications, capabilities, and skills

  • 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 understanding of end-to-end system behavior and downstream impacts, including dependency management, data integrity considerations, back-pressure/capacity constraints, and failure-mode analysis across integrated services.
  • Experience developing or leading cross-functional teams of technologists
  • Experience with hiring, developing, and recognizing talent
  • 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.
  • Practical cloud native experience
  • Expertise in Computer Science, Computer Engineering, Mathematics, or a related technical field