Director of Engineering Operations
Portfolio Delivery Management · Delivery Governance · AI Enablement
I build delivery systems that help engineering teams execute with clarity, accountability, and measurable results.
16 years leading technical programs, PMO governance, compliance delivery, vendor integrations, and engineering operations across SaaS and EdTech platforms.
Established the program management function as the first and only dedicated TPM. Elevated the PMO into a Director-led operating model with formal intake, prioritization, and reporting cadences that improved visibility, accountability, and execution supporting 6 cross-functional teams.
Led 3 teams of 21 contributors, including 11 full-time engineers and 10 contractors, overseeing hiring, onboarding, coaching, and performance management; improved team productivity by 18%, sustained an 89% retention rate, and maintained roadmap delivery coverage.
Delivered PCI DSS and SOC 2 compliance programs, and reduced defect leakage by 20% through CI/CD governance and release-quality controls.
Held authority over architectural decisions and software implementations, and managed Git repositories and merge request review across the development organization.
Directed vendor programs with Affirm, Cybersource, BenchPrep, Thomson Reuters, and LawHub, setting SLAs, resolving escalations, and aligning partner performance with business expectations to achieve 98% SLA attainment.
Modernized operations through AI-enabled tools, reduced manual effort and improved the speed, consistency, and quality of reporting and risk management, saving 30+ hours per month.
A designed operating model showing how engineering delivery changes when AI raises output, and how to govern, measure, and scale it.
A fictional B2B SaaS company of about 30 engineers, across three teams, adopts AI coding assistants. Output climbs, but the lean team has no dedicated delivery function to absorb the new pace. This is the operating model I would design to fix that.
A growing B2B SaaS company of about 30 engineers across three teams adopted AI coding assistants. Output climbed fast, but the company had no dedicated delivery function to manage the new pace.
With lean headcount and informal process, review and QA could not keep up. Predictability slipped and rework grew, while leadership lost a clear line of sight on what would ship and when. Heavy enterprise governance would only have slowed the team down.
A right-sized delivery operating model: lightweight intake and prioritization, paired AI and human review built for a small team, a simple weekly and biweekly cadence, and capacity planning that protects review time. Structure that scales with the company instead of bureaucracy that fights it.
Three squads (Product Owner, AI-enabled Engineers, QA) run delivery, coordinated by a single delivery lead with lightweight governance, and supported by shared Architecture, Security, and CI/CD. Review and integration capacity is planned as a first-class constraint, not an afterthought.
Modeling a 10-engineer team showed AI raising development throughput by roughly 62%, from about 400 to 650 story points. The lesson is the point of the case study: the constraint does not disappear, it moves. Output shifts the bottleneck from development to review and integration, which is where governance and capacity planning have to focus.
| Activity | TPM | Product | Engineering | QA | Security |
|---|---|---|---|---|---|
| Intake & Prioritization | R | A | C | I | I |
| AI-Assisted Planning | A | C | R | C | I |
| Development | A | I | R | C | I |
| AI + Human Code Review | A | I | R | C | C |
| Testing & QA | C | I | R | A | I |
| Release Approval | A | C | R | C | C |
| Risk & Governance | A | C | R | C | C |
| Compliance Controls | C | I | R | C | A |
| Risk | Likelihood | Impact | Owner | Mitigation |
|---|---|---|---|---|
| AI-generated defects and hallucinations | Med | High | Engineering | Mandatory human review gate plus an AI review pass before merge |
| Review and integration bottleneck | High | High | TPM | Plan review as capacity, cap work in progress, rotate reviewers |
| Security vulnerabilities in generated code | Med | High | Security | Automated scanning in CI and security sign-off at release |
| Compliance and control drift | Low | High | Security | Control checkpoints in the workflow with audit-ready evidence capture |
| Knowledge concentration and skill gaps | Med | Med | TPM | Pair review, documentation standards, and onboarding playbooks |
| Technical debt from higher velocity | Med | Med | Engineering | Debt budget per sprint tracked in portfolio governance |
Portfolio case study. Fictional company and modeled figures, built to demonstrate operating-model design. Not a record of actual results.
A single-employer career built on progressive scope, from the first dedicated program manager to Director of engineering operations and the PMO.
Houston, TX · Open to remote, or Houston / Austin hybrid · Available immediately