The work #

Heap is a product analytics platform built on autocapture — the idea that you shouldn’t have to manually instrument every interaction. Instead, the SDK captures user behavior automatically and retroactively, so product teams can answer questions they didn’t know to ask when they shipped the feature. It’s a fundamentally different approach to analytics: instead of “define your events, then collect data,” it’s “collect everything, then ask your questions.”

When I joined, mobile was a growing part of the story but lacked a cohesive technical strategy. The SDK existed, but there wasn’t a clear vision for where mobile analytics was headed or what surfaces Heap should be on next. I established that strategy — and then built the team and architecture to execute it.

What autocapture means technically #

Making autocapture work on mobile is a different kind of engineering challenge than web. On the web, you have a DOM — a structured, queryable tree of elements. On mobile, you’re working with native view hierarchies on iOS and Android that weren’t designed to be observed this way. The SDK has to intercept touch events, identify elements reliably across app updates, serialize interaction data efficiently, and do all of this with minimal performance overhead on the user’s device.

The architecture I drove was built around several hard constraints: capture fidelity had to be high enough for product teams to trust the data; SDK overhead had to be low enough that app developers wouldn’t rip it out; the system needed offline resilience (mobile users lose connectivity constantly, and you can’t lose events); and event batching and transmission had to be smart enough to handle the volume without draining batteries or bandwidth.

At scale, the mobile SDKs were feeding into a pipeline processing billions of events per day. Getting the capture layer right — the part that sits inside someone else’s app — was the foundation everything else depended on.

Expanding the surface #

The bigger strategic bet was this: if Heap’s value is understanding user behavior, and users are increasingly interacting with software on surfaces that aren’t phones or web browsers, then Heap needs to be there too.

I drove the vision and architecture that put Heap analytics on infotainment systems, Roku, Apple TV, and connected devices. This wasn’t just a technical exercise — it required making the case internally that these surfaces mattered, designing SDK abstractions that could work across radically different runtime environments, and convincing enterprise customers that their analytics strategy needed to extend beyond the phone in their pocket.

The mobile product relaunch became the fastest-growing new revenue segment in the company. That’s the part that validated the strategy: it wasn’t just technically interesting, it connected to real business outcomes.

The Contentsquare acquisition #

Heap was acquired by Contentsquare in 2023. I moved into a Senior Engineering Director role overseeing the mobile integration — multiple teams across SDKs, integrations, and mobile analytics platforms.

The core challenge was unifying mobile architecture across two product lines that had been built independently. Heap’s autocapture approach and Contentsquare’s experience analytics (session replay, heatmaps, journey analysis) had complementary strengths but different technical foundations. Merging them without breaking either product’s promises to existing customers was the kind of work that requires equal parts technical judgment and organizational patience.

I built operational rigor around release quality, regression prevention, and measurable performance metrics — the boring-but-essential infrastructure that lets you ship confidently when customers depend on your SDK running inside their production apps. That work significantly reduced churn risk in the first year through quality improvements, reliability initiatives, and a customer-driven roadmap.

The GTM layer #

At Heap, I wasn’t just building — I was explaining. I ran webinars on mobile analytics strategy, supported marketing with technical content, partnered directly with enterprise clients on performance and data fidelity questions, and translated technical decisions into language that customers, execs, and GTM teams could act on.

The ability to tell the story of what you’re building and why — clearly, to different audiences — is a force multiplier. The best technical strategy in the world doesn’t matter if you can’t make it legible to the people who need to buy in.

Why it matters #

Heap was where platform thinking met product analytics at real scale. I proved I could see a wedge (non-traditional surfaces), build the technical and organizational case for it, execute against it with a growing team, and connect it to revenue. The Contentsquare chapter proved I could navigate the messiest part of the lifecycle — an acquisition integration — without losing quality, customers, or people.

The analytics domain itself matters too. Understanding how products are used, where users struggle, what drives retention — this is the substrate that good product decisions are built on. Spending five years deep in that world shaped how I think about building anything.