AdTech runs on billions of events a day, but most growth teams cannot instrument a funnel, ship an experiment, or trust an attribution number. We build the engineering layer – event tracking, experimentation infra, and clean data pipelines – that makes growth decisions provable instead of political.
The Problem
Your event data is a swamp and nobody trusts the dashboard
AdTech platforms generate enormous event volume – bid requests, impressions, clicks, conversions, postbacks – but most growth teams inherit tracking that was bolted on by the ad-serving team for billing, not for product analytics. Definitions drift between teams, the same conversion gets counted three ways, and the CEO and the head of product cite different numbers in the same meeting. When nobody trusts the data, every growth decision becomes an argument instead of a test, and the team defaults to whoever talks loudest.
You cannot ship an experiment without a two-week engineering ticket
Growth in AdTech depends on iteration speed – pricing changes on the SSP, onboarding flows for new publishers, optimization toggles in the DSP UI. But without feature flagging and an experimentation framework, every test requires a custom build, a release train, and a manual analysis. Your growth PM has fifty ideas and the engineering bandwidth to run three a quarter. The roadmap stalls not because the ideas are bad but because the infrastructure to test them does not exist.
Identity deprecation broke your measurement and nobody re-plumbed it
Third-party cookie loss, ATT, and signal loss across Safari and iOS quietly degraded the conversion and attribution data your growth team relies on. Most teams patched the immediate revenue reporting but never rebuilt the internal measurement layer, so growth experiments now run on conversion signals that are 30 to 60 percent under-counted depending on the surface. You are optimizing against a number that is systematically wrong, and the gap widens every quarter as more signal disappears.
Data engineering and growth report to different people and never align
In most AdTech companies the data platform team owns the warehouse and the growth team owns the goals, and the two only talk when something breaks. Growth needs clean, modeled, product-level data; data engineering is busy keeping the bidding pipeline alive at scale. The result is that growth either waits months for the data it needs or builds shadow pipelines in spreadsheets that fall over the moment volume spikes. Neither team is wrong – the operating model is.
How We Help
We start with an instrumentation audit. In the first 30 days we map every event your platform fires, trace it from the ad server or SDK through the warehouse to the dashboard, and document where definitions break. For an AdTech platform this means reconciling the billing-grade event stream with what the growth team needs for product analytics – usually two different views of the same impression and conversion data that have never been formally connected. We come out of the audit with a single tracking plan that engineering, product, and finance all agree on.
Strategy is about deciding what to build first. We do not try to re-architect the entire data platform – we identify the two or three pipelines that unblock the most growth work. Usually that is a clean conversion and attribution layer that accounts for signal loss, a feature-flagging and experimentation framework so the growth team can ship tests without a release train, and a modeled product-analytics dataset that replaces the spreadsheet shadow pipelines. We sequence the build so the growth team gets usable infrastructure in weeks, not after a six-month platform rewrite.
Execution means we embed engineers, not deliver a deck. We implement the tracking plan, stand up the experimentation framework with proper assignment and statistical analysis, and build the data models that feed both the growth dashboard and the experiment readouts. For AdTech specifically, we build the measurement layer to be resilient to identity deprecation – modeled conversions, holdout-based incrementality measurement, and clean separation between deterministic and probabilistic signal so the growth team knows what it can trust. We work inside your stack, whether that is BigQuery and dbt or Snowflake and a homegrown event pipeline.
Measurement is the point of the whole engagement. We instrument the growth program itself: experiment velocity (how many tests the team can actually run per quarter), win rate, time from idea to readout, and the lift attributable to shipped experiments. Growth engineering succeeds when the growth team stops arguing about whose number is right and starts shipping experiments weekly against a metric everyone trusts. The deliverable is not a pipeline – it is a growth team that can move at the speed of its ideas.
What makes this different from a generic data consultancy is that we build for growth outcomes, not data hygiene for its own sake. We do not deliver a beautiful warehouse and leave. We build the specific instrumentation and experimentation infra a growth team needs, embed alongside it for a quarter, and hand off a working system with the team trained to operate it.
What we deliver
- Unified tracking plan reconciling billing-grade and product-analytics event data
- Experimentation framework with feature flagging, assignment, and statistical readouts
- Conversion and attribution layer rebuilt to account for identity and signal loss
- Modeled product-analytics datasets that replace spreadsheet shadow pipelines
- Growth engineering metrics dashboard tracking experiment velocity and shipped-experiment lift
Most AdTech growth teams are not bottlenecked on ideas – they are bottlenecked on the engineering layer that lets them test ideas and trust the result. Build the plumbing and the roadmap unblocks itself.
Our Methodology
Our growth engineering build runs as a 90-day infrastructure installation, not a consulting report. Phase one is the instrumentation audit: we trace every event from source to dashboard, reconcile the billing-grade and product-analytics views of your impression and conversion data, and produce a single tracking plan that engineering, product, and finance sign off on. We identify exactly where signal loss from cookie deprecation and ATT is corrupting the numbers the growth team optimizes against.
Phase two builds the highest-impact infrastructure first. We stand up the experimentation framework with proper feature flagging, randomized assignment, and statistical analysis so the growth team can ship tests without an engineering release train. In parallel we rebuild the conversion and attribution layer to be resilient to identity deprecation, separating deterministic from probabilistic signal so the team knows what it can trust.
Phase three installs the operating cadence and hands off. We embed engineers alongside the growth team for a full quarter, run the first wave of experiments through the new infrastructure, and train the team to operate it without us. Unlike a data consultancy that delivers a warehouse and leaves, we build the specific instrumentation a growth team needs and measure our work by the team's experiment velocity, not by lines of pipeline code.
How We Work
Initial engagements run 3 to 4 months. The first 30 days are the instrumentation audit and tracking-plan design, done alongside your data platform and growth teams. Days 31 to 60 build the experimentation framework and the rebuilt conversion and attribution layer. Days 61 to 90 run the first wave of experiments through the new infra, model the product-analytics datasets, and train your team to operate the system.
Our team includes a growth engineering lead who owns the build, a data engineer who implements the pipelines and tracking plan, and an experimentation specialist who stands up the framework and trains your PMs on statistical analysis. From your side, we need access to the event pipeline and warehouse, time from a data platform engineer for stack-specific context, and a growth PM who will own the experiment roadmap after handoff. We work inside your existing stack rather than introducing new vendors.
Weekly working sessions track build progress against the unblock-the-growth-team milestones. Monthly reviews tie the engineering work to growth outcomes: experiment velocity, the share of decisions made by test versus opinion, and the lift from shipped experiments. Most AdTech companies have a working experimentation framework within 60 days and a trusted measurement layer within 90, with the full compounding benefit visible as experiment velocity climbs over the following two quarters.
- Higher experiment velocity once the team can ship tests without engineering tickets
- A measurement layer the whole company trusts despite identity and signal loss
- Reduced data-engineering load as growth stops building shadow pipelines
- More growth decisions made by test instead of opinion
If your adtech company needs growth engineering leadership, we should talk.
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Frequently asked questions
How much does a growth engineering engagement cost for AdTech companies?
Most AdTech growth engineering engagements run between $25K and $55K per month depending on the state of your existing data stack and how much pipeline and instrumentation work the build requires. That is materially less than hiring a growth engineering lead, a data engineer, and an experimentation specialist as full-time roles, and you get a working system instead of a multi-quarter hiring ramp.
How long before we see results from a growth engineering engagement?
A working experimentation framework is typically live within 60 days, which means the growth team can start shipping tests in the second month. A trusted, rebuilt measurement layer follows by day 90.
How does the growth engineering team integrate with our existing data and product staff?
We embed inside your stack and your cadence rather than working in isolation. We need access to the event pipeline and warehouse and recurring time from one of your data platform engineers for stack-specific context, but we do the build.
What makes Winston Francois different from a traditional data or analytics agency?
Data agencies build warehouses and dashboards as the deliverable and walk away. We build the specific instrumentation and experimentation infrastructure a growth team needs, embed alongside that team for a quarter, and measure our success by their experiment velocity.
How do you measure ROI from a growth engineering engagement?
We instrument the growth program itself: experiments shipped per quarter, time from idea to readout, win rate, and the lift attributable to shipped experiments. The headline metric is how much faster the team can test and how much more of the roadmap turns into measured wins.
What type of AdTech company is the right fit for this service?
Companies with real event volume – a DSP, SSP, exchange, identity platform, or measurement product – where the growth team is blocked by untrusted data or the inability to ship experiments quickly. Series A through growth-stage companies with a product and engineering org but no dedicated growth engineering function see the strongest fit.
