Technical Program Manager

Describe a complex, cross-functional program you launched from strategy through delivery — ideally one with significant business and customer-trust implications.

Also asked as: Tell me about the most complex program you've owned end-to-end, from business case to public launch. · Walk me through a time you launched a major new product or business line against a fixed, public deadline. · Describe a program where the technical execution and the customer-trust implications were equally high-stakes.

OwnershipThink BigInsist on the Highest StandardsEarn TrustDeliver Results

Opening Statement (~60 sec)

I led the end-to-end program to launch an advertising tier on Prime Video — a change to the default viewing experience for 260M+ Prime members worldwide, paired with a new paid ad-free option for anyone who wanted to stay ad-free. This wasn't a feature release. It was a change to the core value proposition of one of Amazon's largest subscription businesses — on a fixed, public date, with zero room to degrade the experience for our 120M+ monthly viewers.

I owned the commercial and technical architecture behind the upgrade path, the rollout sequencing across every viewing platform, and the quality guardrails that governed each stage of the launch — and I ran the cross-functional program across Engineering, Legal, Privacy, Marketing, and Customer Service through a ~14-week delivery window.

The result: we launched on the committed date, reached full global availability within 8 weeks, held 99.99% availability with no degradation in viewing quality, kept advertising levels meaningfully below industry norms, and drove $1.8 billion to $3 billion in incremental ad revenue. What could have been the most disruptive change we'd ever made to a 260M-member product instead became one of the smoothest large-scale launches the organization had run — and the operating model I built — the risk-sequenced rollout, the automated guardrails, the single executive dashboard — became the standing governance framework I ran every subsequent large, cross-organizational program through, not a one-off playbook for this launch alone.

Situation

1. Every major streaming competitor had already launched an ad-supported tier and was monetizing engagement Prime Video wasn't capturing, while our business ran on subscription revenue alone. Leadership set a mandate to close that gap.

2. The mandate wasn't an opt-in experiment — it was a change to the default experience for 260M+ existing members, with a paid upgrade offered to anyone who wanted to stay ad-free.

3. That made it fundamentally different from a normal launch: a defect or a mishandled rollout wouldn't read as a bug, it would read as a broken promise to paying customers. The technical execution and the trust risk were the same problem, not two separate ones.

4. A public launch date was already set before the technical scope was finalized, and the change had to land consistently across every device our members used — none of which had ever carried advertising in this product before.

Task

1. Before any engineering work was scoped, I wrote the program charter and business case myself — scope, success metrics, the executive sponsors who'd own each trade-off decision — so the mandate to launch didn't outrun a documented plan for how.

2. My task was to own delivery of the tier end-to-end: the account and billing changes needed for the ad-free upgrade, the rollout architecture across every viewing platform, and the guardrails that would decide whether each market was ready to expand.

3. I had to bring every platform team to one fixed launch date — without slipping it, and without degrading the experience for our full monthly audience, not just the newly ad-supported segment.

4. Beyond the build, I owned representing the real state of readiness to Legal, Marketing, and Customer Service — none of whom reported to me — so what we told the public matched what we could actually deliver on day one, not an optimistic estimate.

5. My targets: launch on the committed date, hold quality flat against baseline, and have the ad-free upgrade live and self-serve before a single member saw an ad.

Action

  1. 1.Built the commercial architecture for the new default: partnered with Billing and Identity to add a new account state — ad-supported by default, ad-free by upgrade — that resolved in real time with no added latency to the viewing experience, the single hardest constraint, since any slowdown would have touched every stream on the platform, not just the ad-supported ones.
  2. 2.Sequenced the rollout by risk, not convenience: platforms began with the lower-risk delivery path that required no changes on the viewer's device, and only moved to higher-risk, device-side integrations once the underlying system had proven itself in live production.
  3. 3.Made quality the gate, not the dashboard: viewing quality and advertising load were automated stop conditions on every rollout wave — a wave that crossed the line auto-paused before expanding to the next market, with no manual sign-off required to trigger the pause.
  4. 4.Held the advertising level to what the data supported, not what the revenue model wanted — I pushed back on an early proposal to front-load ad inventory ahead of launch, and kept the program to the level validated against viewer drop-off data.
  5. 5.Staged the global rollout by readiness, not by calendar: each market opened only once the prior wave had held its quality guardrails for a full week of live traffic, so the rollout absorbed real-world learning instead of repeating an untested plan at global scale.
  6. 6.Brought Legal, Privacy, and Customer Service into the launch calendar before the public announcement, so customer communication and support readiness moved in lockstep with the technical rollout instead of racing to catch up to it.
  7. 7.Ran a monthly retrospective across every engineering workstream throughout the delivery window and used it to institutionalize process fixes in real time, not just log them — the account-check load-testing requirement and the risk-based platform sequencing were both retrospective outputs, not decisions made once in an initial planning session.

Result

1. Launched on the committed date in the first markets, and reached full global availability within 8 weeks — inside the original plan, with no slip.

2. Held 99.99% availability through launch, including a first-week traffic peak, with no measurable degradation in viewing quality on any platform.

3. Kept advertising levels meaningfully below linear TV and most ad-supported streaming competitors — a specific, defensible answer whenever the question of member impact came up, not a reassurance.

4. The ad-free upgrade was live and self-serve before the first ad ever served, and a high single-digit percentage of eligible members upgraded within 90 days — meaningful scale against a 260M+ member base, with zero billing or entitlement errors.

5. The staged-rollout and guardrail model became the standard playbook for two subsequent pricing and tier changes.

Closing Statement (~60 sec)

What made this a program-management challenge rather than a pure engineering one was that every technical decision doubled as a trust decision. How much advertising was too much, whether to front-load inventory for revenue, how loudly to tell 260M members about a change they hadn't asked for — none of those had a clean engineering answer. They were judgment calls about where to protect long-term trust over short-term metrics.

The principle I carry forward: build the guardrails before you build the growth, and treat the size of a change as a reason to over-communicate, not minimize it. That discipline is what let a change 260M+ members never asked for land as one of our smoothest launches, instead of a trust incident.

Follow-Up Questions

Before the Prime Video story, this is the throughline across 18+ years that gives it context — enterprise cloud and Agentic AI governance at Capital One, global platform delivery at AWS, and mission-critical federal systems before that.

Tell me about yourself and your background.

A ~110–120 second, conversational version of this answer — written to say out loud, not read from bullets. Pause cues are marked inline as (pause).

MY CAREER HAS BEEN BUILT AROUND ONE THROUGHLINE: TURNING COMPLEX ENGINEERING PROGRAMS INTO MEASURABLE BUSINESS OUTCOMES — REVENUE, COST SAVINGS, AND SPEED TO MARKET — AT GLOBAL SCALE.

I'm Venky Chivukula — eighteen-plus years leading enterprise-scale technical programs, most recently as Senior Director of Engineering Programs at Capital One. (pause)

At Capital One, I lead the technical program governance for our enterprise cloud transformation. I built an Internal Developer Platform now running across 2,300+ applications. It's delivered $150 million in infrastructure and tooling savings across 11 lines of business, and driven 60% faster time-to-market for new features and products. Just as important: it gave engineers back 70% of the time they used to spend managing infrastructure — time they now spend building product. (pause)

That includes leading our adoption of agentic AI into how our engineers actually ship code — a governed capability driving a 70% productivity gain across the organization. (pause)

Before Capital One, I spent four years as a Principal Technical Program Manager at AWS, where I drove Prime Video's ad-tech platform to $20 million-plus in monthly incremental revenue, at 99.99% availability, for 120 million-plus global users. I also partnered directly with VP and SVP stakeholders to fix how quickly new publisher partners could start generating revenue with us — cutting onboarding time by 80%, from months down to weeks, so new partnerships started paying off far faster. (pause)

And earlier in my career, I led a $20 million digital modernization program for a financial services enterprise, and separately delivered the Federal Data Services Hub — real-time data exchange serving 16 million-plus citizens across seven-plus federal agencies. (pause)

What's consistent across all of it is the business result, not just the technical delivery: revenue growth, cost reduction, and faster time to market, every time.

That's exactly the kind of business-outcome-driven operating model I'd bring to this role.