Engineering leadership and delivery management
Data and AI teams are notoriously hard to forecast. The work is discovery-heavy, dependencies cross every team boundary, and stakeholders learn to hear “next quarter” as a guess. I’ve spent a decade building the delivery system that fixes this, and I’ve deployed it at 3 companies with the same result: underperforming teams and orgs turned around to reliably hit 95%+ of their sprint, quarter, and program-increment commitments.
The system holds both ends at once. Large initiatives keep moving while the near-term, small, and ad hoc work still ships, through process, measurement, and tooling rather than heroics.
What’s usually broken
- Forecasts are guesses. Estimates come top-down or bottom-up, never both, and never reconciled against what the team actually shipped.
- Priorities change weekly. Whoever escalates loudest wins, so the roadmap is a suggestion and the team learns not to believe it.
- Utilization is invisible. Nobody can say how much capacity exists, how much is committed, and how much quietly disappears into unplanned work.
- Agile theater. The ceremonies run on schedule while delivery dates keep slipping, because process was installed without the measurement that makes it mean anything.
- The org shape fights the work. Teams aligned to projects instead of products, specialists buried inside generalist teams, contractors where the durable knowledge should live.
What I do
- The delivery system: a custom-scaled agile method built specifically for data and AI work. Top-down and bottom-up estimation reconciled sprint by sprint, trailing month-over-month and quarter-over-quarter analysis, a modified RICE scoring model, 4-tier prioritization, structured risk tracking, and a weekly-monthly-quarterly business review cadence. The output is quantified, forecastable work: commitments the business can plan against.
- Delivery turnaround: installing that system in an existing org, including the measurement baseline, the operating cadence, and the stakeholder contract that makes prioritization stick.
- Org design: team topology, role families, product-aligned structures, hire-versus-train-versus-contract decisions, and executive hiring, including writing the JDs and competency models.
- Leadership advisory: a standing sounding board for your data or engineering executive, or for the founder currently doing that job unofficially.
- AI-era delivery: layering AI-assisted development onto the delivery discipline. At TelevisaUnivision that combination compounded to a 35% throughput gain by year 2, on the same headcount.
How it works
The system installs in stages: measurement baseline first, then estimation discipline, then the review cadence, with the metrics visible to everyone from sprint one. It reaches useful accuracy fast. 2 quarters to cross 90% on-time delivery at both Goldman and TelevisaUnivision, and it’s tracking the same curve across 6 teams and 3 regional tenants at AXS.
The certifications behind it: Certified SAFe 4 Agilist, Certified ScrumMaster, and Certified Scrum Product Owner. The system itself came from years of running data and AI delivery end to end, and it borrows from all 3 frameworks without belonging to any of them.
Proof
Goldman: crossed 90% repeatable delivery within 2 quarters and peaked near 95% across 5 teams, with sprint forecast accuracy improved 2-3x. TelevisaUnivision: over 90% within 2 quarters, throughput compounding 7-9% per quarter to roughly 35% by year 2, worth an estimated $1.5-2.25MM a year in recovered capacity on a 30-person org. AXS: over 85% within 2 quarters across a 6-team, 3-tenant program, tracking the same curve.
Start a conversation
If your roadmap dates are guesses, I can make them commitments. Ask me how the system would land in your org.