Director Engineering Effectiveness Hybrid in Horsham, PA

RELX•Horsham, PA

14h•Hybrid

About The Position

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. The Director will lead existing capabilities while helping define how engineering teams work in an environment where agents play an increasing role in code generation, testing, refactoring, and workflow execution. This leader will help the organization move beyond optimizing legacy engineering practices and instead build the mechanisms, guardrails, capabilities, and ways of working needed for durable AI-first software development. This role partners closely with engineering leaders across product engineering, platform engineering, infrastructure, security, and planning. It does not replace engineering leader accountability for delivery within their organizations. Instead, it owns the cross-cutting capabilities, practices, and enablement needed to help teams deliver more effectively and consistently across the organization.

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
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

Nice To Haves

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

Responsibilities

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
Build durable mechanisms for continuous improvement rather than one-time transformation efforts
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
Lead the AI Enablement function focused on transforming how engineering teams work through AI-assisted and agent-enabled engineering workflows
Define and scale AI-first engineering practices that reflect how agents and AI tools are changing software development, testing, refactoring, and developer workflows
Identify high-value engineering use cases where AI-assisted and agent-enabled approaches can materially improve speed, quality, developer experience, or delivery effectiveness
Drive adoption of modern developer tooling, AI-assisted coding practices, and agent-enabled workflow patterns that deliver measurable value to engineering teams
Establish practical standards, guardrails, review patterns, and quality controls for safe and effective use of AI in software engineering in partnership with security, platform, legal, and other relevant leaders
Help the organization move from isolated experimentation with AI tools to durable changes in engineering workflows, team practices, and operating models
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
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
Lead 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
Provide engineering leaders with clear visibility into delivery health, sequencing options, and execution risks
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
Establish common quality engineering expectations, patterns, and scorecards where they create meaningful value
Ensure quality is embedded throughout the software development lifecycle and treated as an engineering capability, not a downstream gate
Partner closely with engineering leaders across product engineering, platform engineering, infrastructure, security, and planning to improve engineering outcomes
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