Manager, Engineering Operations

Engineering Operations Lead

The Engineering Operations (EngOps) Lead is responsible for improving the effectiveness and reliability of the software development lifecycle across engineering teams. The role focuses on engineering productivity, delivery processes, release management, and operational discipline across the engineering organization.

The role also evaluates and enables AI-assisted engineering capabilities that improve developer productivity, software quality, testing efficiency, release reliability, and overall engineering effectiveness.

Working closely with engineering leadership, platform engineering, and product management, the EngOps Lead establishes frameworks, tooling, and practices that enable engineering teams to deliver software consistently, with high quality and operational reliability.

This role does not manage infrastructure operations but focuses on improving the systems and processes through which engineering teams build, test, release, and operate software.

Key Responsibilities & Duties:

Engineering Delivery Framework

• Establish and maintain standardized software delivery practices across engineering teams.
• Define engineering lifecycle processes including planning, development, testing, release, and operational handoff.
• Improve engineering predictability through consistent delivery practices and release governance.

Developer Productivity

• Identify opportunities to improve developer productivity and engineering workflow efficiency.
• Define engineering lifecycle processes including planning, development, testing, release, and operational handoff.
• Evaluate and implement AI-enabled developer tools and engineering automation capabilities that improve coding efficiency, knowledge sharing, troubleshooting, and software delivery performance.
• Establish standards and best practices for responsible use of AI-assisted development tools within the software engineering lifecycle.
• Reduce friction in the development lifecycle by improving tooling, documentation, and internal engineering services.

Engineering Automation and AI Enablement

• Identify opportunities to leverage AI, automation, and intelligent tooling to improve software development workflows.
• Evaluate AI-assisted coding, testing, documentation, code review, and operational support capabilities.
• Partner with engineering leadership to establish standards, governance, and adoption practices for AI-enabled engineering tools.
• Measure the impact of AI-assisted development practices on productivity, software quality, and delivery performance.
• Ensure AI-enabled engineering capabilities align with organizational security, compliance, and architecture requirements.

Release and Change Management

• Establish release coordination practices for engineering services and platforms.
• Improve release reliability through automated testing, deployment practices, and release governance.
• Partner with Service Operations to align engineering release processes with enterprise change management.

Engineering Metrics and Performance

• Define and track engineering delivery metrics such as: deployment frequency, lead time for changes, change failure rate, mean time to recovery.
• Provide visibility into engineering performance and delivery health through dashboards and reporting.
• Measure and report on the effectiveness of engineering automation and AI-assisted development capabilities.

Engineering Process Improvement

• Identify systemic inefficiencies in engineering workflows and implement improvements.
• Drive adoption of engineering best practices including version control standards, testing strategies, and deployment practices.
• Support continuous improvement across the engineering organization.

Collaboration Across Technology Functions

• Partner with Product Management to support predictable delivery of product roadmaps.
• Work with Platform Engineering to improve build systems, developer tooling, and automation frameworks.
• Partner with Enterprise Architecture, Product Management, and Security teams to evaluate and operationalize AI-enabled engineering capabilities.
• Coordinate with Infrastructure and Service Operations to ensure engineering releases integrate smoothly into operational environments.

Required Knowledge, Skills, and Abilities:

• Bachelor's or Master's degree in Computer Science, Engineering, or related field.
• 810 + years of experience in software engineering, developer platforms, or engineering operations roles.
• Strong understanding of modern software development practices including CI/CD, automated testing, and DevOps methodologies.
• Understanding of AI-assisted software development tools, intelligent automation, and emerging engineering productivity technologies.
• Experience evaluating and implementing developer productivity platforms, engineering automation solutions, or AI-enabled development workflows.
• Experience improving software delivery processes across multiple engineering teams.
• Familiarity with modern development tools, version control systems, and release automation platforms.

Preferred Qualifications:

• Experience operating within large-scale distributed engineering organizations.
• Familiarity with DevOps, SRE, and modern software delivery practices.
• Experience implementing AI-assisted coding, testing, documentation, or software delivery platforms.
• Familiarity with emerging AI technologies supporting software engineering and developer productivity.
• Experience implementing engineering metrics and delivery performance frameworks.
• Knowledge of cloud-native development environments and distributed system operations.

Key Competencies:

• Systems thinking and process design
• Strong collaboration with engineering teams
• Data-driven improvement of engineering delivery
• Ability to balance operational discipline with engineering agility