Director, Engineering - Growth & Offering Enablement

Gravie

4d•$180,000 - $250,000

About The Position

Gravie is seeking a hands-on, AI-forward Director of Product Engineering to lead their Growth and Offering Enablement pod. This team builds products and capabilities that help Gravie win and retain business, improve broker and account-management productivity, shorten the sales lifecycle, and bring new products, networks, and plan designs to market faster. The pod owns end-to-end product capabilities across this domain and uses technologies best suited to each problem, including Salesforce, custom applications, services, integrations, workflow automation, data products, and AI-enabled experiences. As the engineering leader, you will partner closely with Product Management and leaders across Sales, Account Management, Underwriting, Operations, Data, and Technology. This is a hands-on leadership role involving architecture, technical design, code and configuration reviews, prototyping, and solving complex engineering problems. You will also help define how engineering is performed in an AI-native organization, looking for someone who has moved beyond using AI as a coding copilot and can demonstrate how agentic development has improved the speed, quality, and effectiveness of software delivery.

Requirements

Significant experience leading product engineering teams responsible for business-critical products, workflows, or customer and employee experiences.
A strong software-engineering background and the technical depth to engage credibly in architecture, design, implementation reviews, debugging, and incident response.
Experience building or materially transforming an engineering team, including hiring, coaching, performance management, and developing technical leaders.
Experience partnering with Product Management and business leaders to translate strategy into roadmaps, technical plans, and measurable outcomes.
Experience delivering capabilities that span custom software, configurable products, APIs, integrations, automation, data, and cloud technologies.
Meaningful experience with Salesforce architecture, development, configuration, automation, integrations, security, data, and release management.
A track record of improving delivery predictability, engineering quality, reliability, and long-term technical health.
Demonstrated experience applying AI across the software development lifecycle, including implementing agentic workflows with appropriate security, evaluation, and human oversight.
Strong technical and product judgment, including the ability to balance speed, complexity, cost, risk, and maintainability.
Strong communication skills, with the ability to explain complex technical decisions clearly to engineers, business leaders, and executives.

Nice To Haves

Healthcare, benefits, insurance, financial services, or another regulated industry.
Salesforce Sales Cloud, Service Cloud, Health Cloud, or related technologies.

Responsibilities

Build, lead, and develop a high-performing product engineering team with expertise across software development, Salesforce, integrations, automation, quality engineering, and cloud technologies.
Partner with Product Management to establish the vision, roadmap, and measurable outcomes for Growth and Offering Enablement.
Own engineering delivery from discovery and technical planning through implementation, release, measurement, and production support.
Set technical direction and make sound architecture and technology decisions across Salesforce, custom software, integrations, automation, and third-party solutions.
Establish modern engineering practices, including source control, peer review, automated testing, CI/CD, observability, security, incident management, and incremental delivery.
Improve Salesforce engineering within the domain, including development, configuration, automation, integrations, data, testing, release management, and governance.
Ensure Salesforce configuration and metadata are treated as version-controlled, testable, and reviewable engineering assets while avoiding unnecessary customizations.
Communicate priorities, progress, risks, dependencies, and technical tradeoffs clearly to engineering, business, and executive stakeholders.
Define and advance agentic engineering practices across the software-development lifecycle.
Design agentic workflows that complete bounded, multi-step engineering tasks using approved tools, relevant context, automated validation, and human oversight.
Apply agents to requirements analysis, implementation planning, impact analysis, code and configuration generation, automated testing, code review, documentation, defect investigation, and release validation.
Identify opportunities to redesign engineering workflows around AI rather than adding AI to inefficient existing processes.
Establish appropriate controls for data protection, security, permissions, traceability, testing, evaluation, and human approval.
Measure the effect of AI adoption on delivery speed, quality, cost, developer experience, and operational performance.
Personally demonstrate agentic development through working examples, prototypes, or production workflows.