Director Engineering Effectiveness*** Hybrid in Horsham, PA

About the role

The Director, Engineering Effectiveness will lead a cross-functional organization focused on improving how engineering teams deliver software across the business and on shaping the engineering operating model for an AI-first future. This leader will own Delivery Excellence, Quality Enablement, and AI Enablement, with accountability for improving engineering effectiveness, delivery predictability, release confidence, and driving the organization’s progression from teams using AI tools alongside traditional practices, through AI-led workflows with human oversight, toward increasingly autonomous delivery at scale.

About the team

Reporting to the CTO, the Director, Engineering Effectiveness will lead a cross-functional organization focused on improving how engineering teams deliver software across the business and on shaping the engineering operating model for an AI-first future. This leader will own Delivery Excellence, Quality Enablement, and AI Enablement, with accountability for improving engineering effectiveness, delivery predictability, release confidence, and driving the organization’s progression from teams using AI tools alongside traditional practices, through AI-led workflows with human oversight, toward increasingly autonomous delivery at scale.

Conditions of employment

• You must be a U.S. citizen to apply for this position.
• You must successfully pass a background investigation and achieve Public Trust security clearance.
• Must be located near the Horsham, PA location for a Hybrid onsite schedule.

Accountabilities

• Lead the engineering effectiveness agenda across the organization, aligned to the CTO's strategic priorities.
• Translate engineering leadership priorities into clear operating rhythms, improvement plans, and measurable initiatives.
• Establish engineering scorecards and review cadences that improve visibility into delivery health, quality, execution risk, and engineering effectiveness.
• Define common expectations and fit-for-purpose ways of working where greater consistency improves engineering outcomes.
• Use data, team feedback, and operational observation to identify bottlenecks, reduce friction, and improve how engineering teams plan, build, test, and deliver software in both traditional and AI-assisted development environments.
• Recommend and shape the additional capabilities, roles, and support needed to improve effectiveness at scale.
• Define effectiveness practices designed for small, durable delivery teams, including how quality gates, delivery rhythms, and planning practices apply at that scale across a portfolio of products.
• Define what good looks like at each stage of AI engineering adoption, establish clear benchmarks for measuring where teams are, and drive measurable team progression against those benchmarks.
• Assess where additional tooling, expertise, or organizational support is needed to accelerate adoption and impact.
• Own the definition, adoption, and scaling of spec-driven engineering practices, including the governed toolchain that enforces architecture, security, and quality standards across AI coding tools used by engineering teams.
• Help the organization move from isolated experimentation with AI tools to durable changes in engineering workflows, team practices, and operating models.
• Own the Delivery Excellence function, including scrum masters, delivery leads, and related delivery support roles.
• Improve planning quality, execution discipline, dependency management, and delivery predictability across teams and portfolios.
• Implement delivery practices that improve outcomes without overburdening teams with unnecessary process.
• Support engineering leaders in improving forecasting, coordination, flow, throughput, and delivery reliability while preserving their accountability for delivery outcomes.
• Lead the Quality Enablement function as an engineering capability focused on increasing engineering ownership of quality.
• Improve test strategy, automation practices, release readiness, defect prevention, and root-cause learning across product and platform teams.
• Partner with engineering leaders to increase release confidence and reduce escaped defects, production issues, and quality-related rework.
• Ensure quality is embedded throughout the software development lifecycle and treated as an engineering capability, not a downstream gate.
• Collaborate with leaders to ensure delivery, quality, and AI enablement practices support real engineering needs and business priorities.
• Coordinate with platform and architecture leaders where standards, tooling, developer workflows, and shared engineering services intersect.
• Partner with infrastructure, operations, and security leaders on release readiness, operational effectiveness, engineering controls, and safe adoption of modern development practices.
• Align engineering effectiveness practices with planning, sequencing, and cross-functional delivery reviews to improve forecast quality and reduce execution friction.
• Support the CTO and senior leadership team with engineering effectiveness insights, recommendations, and progress reporting.

Requirements

• 10+ years of experience in software engineering, engineering leadership, engineering effectiveness, developer productivity, quality engineering, or related technology leadership roles.
• Experience leading engineering teams or engineering leaders and driving improvement through data, metrics, and operating rhythms.
• Demonstrated success improving engineering delivery, quality outcomes, and organizational effectiveness across multiple teams or portfolios.
• Strong understanding of modern software engineering practices, including software delivery, quality engineering, DevOps, developer workflows, operational excellence, and the implications of AI-assisted and agent-enabled development.
• Strong proficiency with AI-assisted and agent-enabled software development, with the ability to evaluate tools, workflows, and engineering implications with credibility.
• Demonstrated experience leading engineering teams that have shipped AI-assisted or agent-enabled workflows into production, not just piloted or experimented with them.
• Strong understanding of how AI is changing engineering productivity, quality, team practices, and operating models.
• Experience leading cross-functional engineering improvement initiatives in a matrixed environment.
• Strong ability to influence engineering leaders and drive adoption of better ways of working through credibility, collaboration, data, and practical execution.
• Strong people leadership capability, including building and developing high-performing cross-functional teams.
• Demonstrated ability to partner effectively across engineering, infrastructure, security, and business stakeholders.

Preferred qualifications

• Experience leading delivery excellence, quality enablement, engineering effectiveness, developer productivity, or related cross-functional engineering functions.
• Experience building or reshaping organizations to support engineering effectiveness and modern software delivery practices at scale.
• Experience leading AI enablement, engineering productivity, or engineering transformation efforts in software engineering organizations.
• Experience defining engineering practices, guardrails, or operating models for AI-assisted and agent-enabled development at scale.
• Experience improving engineering workflows in multi-product, platform, or portfolio-based engineering organizations.
• Experience partnering with CTOs and senior engineering leaders on strategic priorities and organizational improvement.
• Demonstrated technical credibility with software engineering leaders and teams.
• Experience working in a hybrid remote/on-site schedule.