Chris Walker

I find out what's actually going on.

Then I help the business see it, understand it and act on it. Sometimes that starts with a blank page. Sometimes it means picking up something half-built and working out what it needs next. I'm at home with either.

Most questions reach a data team already tangled: a product decision wrapped round a pricing problem, wrapped round a number nobody quite trusts. Pulling harder doesn't help. I work out which thread to start with, what to save for later and what to leave alone, and I bring the people who own the decision along with me.

Since 2017 that has been at Overleaf and Digital Science: marketing and product analytics first, then Head of Analytics, now Director of Analytics. I built the data platform and the team, and I still write the SQL. Before that I was a systems engineer, ran two businesses of my own, and spent years as the marketer asking for the numbers. I've sat on both sides of the table.

Now: Director of Analytics, Digital Science. Six products, five market segments, nine-figure revenues.
Before: Head of Analytics, Overleaf. Built the function, the platform and a team of six.
Most use: Where there's a real business and a real product, and more questions than trusted answers. Mature data foundations optional.
Works with: Product, commercial and leadership teams. Board to engineers, in the language each of them uses.
Hands-on: BigQuery, Dataform (dbt), SQL, Python, Looker, Mixpanel, and agents running in production.
Education: MEng, University of Cambridge.

Five times it wasn't what it looked like

Each of these started as one problem and turned out to be another. The last line of each is what's true now.

2021 to 2024OverleafPlatform and experimentation

From 25 questions to 1.5 billion events a month

It looked like: Overleaf needed proper analytics. I'd been working on its marketing and product numbers for a few years, everyone had questions, and the obvious move was to buy a tool and start building dashboards.
What was going on: The data lived in silos: three backend databases, a payment provider and Salesforce, with no way to connect what users did to what they paid. And there were 25+ questions on the table, with no agreement about which mattered most.
What we did: I gathered the questions from across the business, grouped them into themes and chose the first five to answer. That was enough to prove the idea and show what the platform had to do. It went up in layers on BigQuery and Dataform: raw data as received, a clean layer, then a modelled layer the business could use. A split-testing method went on top, so product decisions could be tested and not argued.

Now1.5 billion events a month, a team of six, and 94 experiments run through a framework that became part of how product ships. Every other story on this page runs on it.

BigQuery underneath, Dataform (dbt) for the modelling, Looker and Mixpanel on top.

2021 to 2024OverleafWays of working

A seat at the table

It looked like: A new analytics team, and a product organisation ready to send it requests. The usual model: a queue, a dashboard, a thank you.
What was going on: By the time a request reaches a queue, the question has already been framed, often around the wrong thing. An analyst is most useful in the room where the problem gets defined.
What we did: I positioned analytics as the quantitative mirror of design. Design brings the user's voice. Analytics brings what users actually did. Analysts became mandatory in squad planning every Shape Up cycle, sometimes as a fourth member of the product trio, sometimes in place of a designer or an engineer.

NowOn a project about compile errors, the analyst in the first planning session redirected the work from the interface to the compile logs, and saved a good deal of user research. The model spread to more squads, and analysts have been part of planning ever since.

The analyst mirrors the designer: present when the question is framed, not after.

2022 to 2023OverleafPricing

The winning test that was losing money

It looked like: A success. A change to the upgrade page had been A/B tested, and subscription starts and revenue per user both went up.
What was going on: Over the following months people were choosing the cheapest plan in place of the ones above it, and losing the collaboration features we most wanted them to use. That plan grew to over 7% of billings. Lifetime-value analysis put the cost at five figures a month, and compounding.
What we did: My job was to equip the people making the decision. I set out four options with the impact of each and took the CEO, CFO, CMO and CTO through the analysis, facts first, answering "can we trust this?" before anything else. Keeping the plan would have been a reasonable call if the strategy had pointed that way. It didn't.

NowThe plan was retired. ARPU and lifetime value moved as the model predicted, and subscriptions fell less than anyone feared. Tests aren't designed to catch an effect like this, and we still need to test quickly. So where a change carries a risk of one, we now follow the longer-term trend after the test ends.

Four options, four executives, one decision. Theirs to make.

2023 to 2025OverleafSales enablement

The customers sales couldn't see

It looked like: A thin B2B pipeline. Sales needed more leads.
What was going on: The leads were already customers. Thousands of people inside universities and companies were paying individually, and the evidence was spread across four systems nobody could join. Preparing for one call took hours, and often an engineer.
What we did: Discovery first, then an MVP with the five numbers the sales team actually used. The awkward transaction types were left out and labelled as a limitation. It worked, and people wanted more. I didn't try to do everything and do it badly. I built the business case and held the tool steady until the business was ready to back it with a dedicated analyst. Then we rebuilt it properly.

NowAround 50 people use it every month, and its reach goes well beyond the sales team it was built for. It surfaced large organisations nobody knew were customers, and the sales team say they "always have the tab open".

An MVP first, then a business case, then production. Eighteen months end to end.

2026Digital ScienceAgentic systems

Agents for the routine, analysts for the judgement

It looked like: A capacity problem. A backlog of data modelling nobody had time for, and a steady stream of questions from product teams.
What was going on: Most of those questions were routine, and the routine was crowding out the work only an analyst can do: deciding what to measure, working out why something moved, and telling a product team what to do about it.
What we did: It started as two experiments against live BigQuery: an agent that answered questions in plain English, and one that drafted metric designs from GitHub issues. What made them worth trusting was the groundwork underneath: a well-modelled metrics layer with agreed definitions, so the agent reads from the same answers an analyst would. Then came a framework for the harder cases, so that when the answer isn't sitting in the metrics layer the agent knows how to find its way round the warehouse and dig.

NowPeople ask a question in Slack and an agent answers it, from the metrics layer where it can and by digging deeper where it can't. Drafting a design from an issue, or building the model from one, is routine. Most of the product teams' questions are handled this way, by agents the team built and maintains, and the analysts spend their time on more impactful analysis.

The metrics layer is the part that makes the answers worth trusting.

I find out what's actually going on.

How I go about it

Find the real question.

Start with the end in mind.

Focus first.

Think once.

Trust matters.

Equip the decision.

If nobody uses it, it didn't happen.

People keep the judgement.

Five times it wasn't what it looked like

From 25 questions to 1.5 billion events a month

A seat at the table

The winning test that was losing money

The customers sales couldn't see

Agents for the routine, analysts for the judgement

The numbers

Four jobs before this one

Systems engineer

Founder

Analytics and digital strategy consultant

Marketing and product analytics

If something's tangled, I'm happy to take a look.