damian rzepecki

Damian Rzepecki's engineering career reveals a blueprint for scaling growth platforms without sacrificing observability - and his architectural decisions at Pinterest and Mabl still hold lessons for modern tech stacks.

When senior engineers debate the trade-offs between feature velocity and system stability, the name damian rzepecki often surfaces in conversations about growth engineering done right. His tenure at Pinterest as Head of Growth Engineering, followed by his CTO role at Mabl and VP Engineering at Vox Media, provides a rare longitudinal case study of how an engineer applies platform-thinking to both consumer and developer tools.

This article unpacks the technical decisions Rzepecki championed, the specific infrastructure choices that defined his project and what senior engineers can learn from his approach to integrating experimentation, monitoring. And data pipelines. We will avoid biography for its own sake; instead, every section ties back to verifiable systems, RFC-level reasoning. And production realities.

Growth Engineering Infrastructure: The Pinterest Experimentation Platform

At Pinterest, Damian Rzepecki oversaw the growth engineering group responsible for building the A/B testing framework that powered user acquisition, retention. And engagement features. One of the lesser-discussed but critical components was the real-time event pipeline that fed experiment results back to product teams. Instead of relying on batch processing, Rzepecki's team built a Kafka-based stream processing architecture using Apache Samza - a choice that allowed near real-time allocation of traffic to experiment variants.

The key insight here is not just the choice of tech stack but the data quality controls baked into the pipeline. In production, we observed that many experimentation platforms fail not on the statistical engine but on the consistency of event counts across client and server events. Rzepecki's team implemented idempotent event handling using unique user-session keys, which drastically reduced instrumentation drift - a common pain point that still plagues many growth engineering teams today.

Another architectural decision worth noting is how they handled differential privacy for sensitive user data used in growth experiments. Rather than applying a blanket set of rules, Rzepecki's team integrated Google's RAPPOR protocol into the event collection layer, enabling aggregation of user behaviors without exposing individual-level data. This was forward-looking, executed before GDPR became a compliance razor.

Observability as a First-Class Concern in Growth Stacks

One of Rzepecki's consistent themes across roles is that observability can't be an afterthought - especially in growth engineering where every feature toggle or experiment can silently degrade user experience. At Pinterest, his team implemented a custom monitoring layer on top of Prometheus to track not just system health but also experiment health. Metrics like "user-visible latency per experiment variant" and "error rate per feature flag" were surfaced in dashboards that product managers could read alongside engineers.

This approach foreshadowed what later became the discipline of "observability-driven development" - where monitoring requirements are defined before the feature code is merged. In practice, Rzepecki's team used OpenTracing (before OpenTelemetry became the standard) to propagate trace context Across the microservices that powered growth flows like sign-up, onboarding. And notification delivery. Traces allowed engineers to pinpoint whether a slowdown originated from the recommendation service, the push notification gateway. Or the database layer.

The lesson for senior engineers is clear: growth engineering needs deep observability because the blast radius of a bad experiment can be millions of users within minutes. Rzepecki's insistence on instrumenting every microservice with structured logging and trace IDs is now common wisdom but in the mid-2010s it was a deliberate architectural choice that differentiated Pinterest's growth platform.

Transitioning to Developer Tooling: Mabl's Test Automation Architecture

When Damian Rzepecki became CTO at Mabl, the company was building a no-code test automation platform for web and mobile applications. This transition from consumer growth engineering to developer tooling is instructive because it reveals how Rzepecki applied his platform engineering mindset to a completely different domain - quality assurance infrastructure.

Mabl's architecture relied on a hybrid approach: a browser-based recording tool that generated test scripts, combined with a cloud execution engine that ran those scripts in parallel across headless browsers. Rzepecki drove the decision to move from a monolithic test runner to a containerized approach using Docker and Kubernetes. Each test was executed in an isolated container, allowing Mabl to scale from hundreds to thousands of parallel test executions without resource contention. This is a classic platform trade-off: the overhead of container orchestration was justified by the reliability guarantees for customers.

Beyond infrastructure, Rzepecki emphasized test data management as a first-class problem. He blogged about how Mabl's system automatically generated and rotated test data to avoid flaky tests caused by stale data. This involved integrating with cloud databases like Amazon RDS and using deterministic seeding of test states - a pattern that many CI/CD pipelines still miss today.

Data Engineering Decisions at Vox Media: From CMS to Content API

At Vox Media, as VP Engineering, Rzepecki oversaw the evolution of the content management platform that powered sites like The Verge and Vox com. A particularly technical decision involved migrating from a monolithic CMS to a headless content architecture using Contentful and a custom GraphQL layer. This allowed editorial teams to write once and publish across web - mobile app. And third-party platforms like Apple News and AMP.

The data engineering challenge here wasn't just the API layer but the content personalization pipeline. Rzepecki's team built a real-time recommendation engine using event streaming from a mobile app's clickstream to update article suggestion models. The architecture used Apache Spark for batch model training and scheduled airflows for feature engineering, with the trained models served via a Redis-backed inference service.

One aspect that stands out to us as engineers is the attention to cache invalidation across a multi-region CDN. Vox Media used Fastly as its CDN, and the team implemented surrogate keys that allowed granular cache purges per content type. When a breaking news story was published, the cache key for that category was invalidated, pushing fresh content to edge nodes without purging the entire cache. This is a textbook example of balancing freshness with performance - a challenge that Rzepecki addressed by instrumenting cache-hit ratios and alerting on stale-content incidents.

Platform Policy Mechanics: Consent and Compliance Automation

Damian Rzepecki's work at Vox Media also intersected with the growing complexity of privacy regulations. He led the engineering effort to build a consent management platform (CMP) that could handle GDPR, CCPA. And future regulations without requiring manual code changes per publication. The system was built as a middleware layer that intercepted tracking scripts and user-consent signals before any data left the browser.

Technically, the CMP used a declarative policy engine based on Rego (from Open Policy Agent) to define which vendors could be loaded based on user choices. This allowed legal and product teams to update consent rules without restarting the web application. The integration with Google Tag Manager and multiple ad servers meant that the CMP had to synchronize consent states across iframes and subdomains - a notoriously tricky cross-origin messaging problem. Rzepecki's team solved this using postMessage APIs with origin validation, a pattern documented in the W3C Web Messaging specification.

For mobile apps, the same CMP logic was embedded in a native SDK that communicated with the web view via a JavaScript bridge. This shows an engineer who thinks holistically about platform policy mechanics, treating compliance not as a checkbox but as a distributed systems problem.

The SRE Mindset in Damian Rzepecki's Career

Reading through Rzepecki's public talks and technical posts, a consistent theme emerges: he treats reliability as a product of engineering choices, not just operational heroics. At Pinterest, he advocated for circuit breakers in the growth API layer to prevent cascading failures when the recommendation service degraded. At Vox Media, he implemented staged rollouts for all CMS changes using feature flags from LaunchDarkly, with automatic rollback if error budgets were exhausted within the first hour.

This SRE mindset is encoded in the way he built teams. Rather than separate SRE and development teams, Rzepecki embedded reliability responsibilities into every squad. On-call rotations included product engineers who had written the code. And post-incident reviews were blameless - but detailed RFCs were required to document the architectural root cause. We find this approach aligns with Google's SRE book recommendations, particularly the concept of "error budgets" that balance feature velocity with reliability.

Lessons for Building Scalable Growth Platforms Today

After examining Damian Rzepecki's career moves and technical decisions, we can distill several actionable patterns for senior engineers:

• Instrument everything you experiment on. Every feature flag should emit metrics on usage, errors,, and and latency before it's fully releasedUse OpenTelemetry to standardize tracing across services.
• Treat compliance as a systems integration problem. Build consent middleware that sits between user-facing code and third-party vendors, rather than sprinkling if-statements across every page.
• Prefer containerized test execution for CI/CD. The isolation pays dividends in determinism, even if the orchestration overhead seems high initially.
• Cache aggressively but with fine-grained invalidation triggers. Use surrogate keys with CDNs and monitor cache-hit ratios as a business metric.
• Automate data quality checks in A/B pipelines. Idempotent events and differential privacy controls should be part of the infrastructure, not an afterthought.

Frequently Asked Questions About Damian Rzepecki's Engineering Approach

What technical stack did Damian Rzepecki use at Pinterest for growth experiments?

His team used a Kafka and Apache Samza streaming pipeline for real-time experiment event processing, with Prometheus for monitoring and custom metric dashboards. They also integrated Google's RAPPOR for differential privacy.

How did Rzepecki handle test data management at Mabl?

Mabl implemented automatic test data generation and rotation using deterministic seeding, with integration to Amazon RDS for database state management, ensuring flaky tests were minimized.

What was the key challenge in building a consent management platform at Vox Media?

Cross-origin consent synchronization across iframes and subdomains required careful use of the postMessage API with origin validation, plus a native mobile SDK for app-to-web communication.

How did Rzepecki ensure growth experiments didn't degrade user experience?

He instrumented each experiment variant with its own latency and error rate metrics. And used circuit breakers in the growth API layer to isolate failures from cascading.

What is one architectural lesson from Rzepecki's career that applies today?

Ensuring idempotent event handling in experimentation pipelines is critical to avoid data drift that invalidates statistical results - a lesson many teams still learn the hard way.

What do you think?

How should senior engineers balance the overhead of real-time experimentation pipelines against the cost of batch processing, given that most growth teams operate under tight infrastructure budgets?

If you were rebuilding Mabl's test runner today, would you still use Docker and Kubernetes,? Or would you consider serverless functions for even simpler scaling?

Given the shift toward privacy-first platform policies, do you think consent management middleware should be open-sourced as a standard layer, like OpenTelemetry?