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You will design and build the data infrastructure and monetization products that enable retailers to sell advertising on their own platforms. This involves creating canonical event models, identity resolution systems, and attribution frameworks to turn customer interactions into revenue.
See something that should exist? Build it.
See a better way? Do it.
At Terrific, every role directly impacts how we grow from product to revenue to customer experience. We’re building the next generation of social commerce through live shopping powered by real-time data and AI.
And we believe customer data should belong to the brands who earn it — not third-party platforms.
Europe · Remote · Full-time Contract
Product & Engineering · Commerce Media · Data · AI
Start date: November 2026
See data that should be worth money? Make it worth money.
See a number nobody can explain? Go find out why.
At Terrific, we're building the next generation of social commerce—helping brands and publishers turn content, creators, live experiences and AI into commerce they own.
Our technology brings shoppable video, live shopping, interactive timelines, polls and product carousels directly onto a merchant's website or app.
Now we're looking for a Product Engineer to turn what those experiences already know about a shopper into a monetization product—and to decide what that product should be.
This is not a role where you'll inherit a roadmap.
You'll write it.
Terrific sits on something most advertising companies cannot get. On a single customer's domain we see the video someone watched, the poll they voted in, the product they tapped, the coupon they used, what went into the cart and what they actually bought—first-party, consented, tied to a real catalog and a real checkout.
Most advertising systems have one half of that and model the other half.
Nobody has turned it into a product yet.
That's this role. You'll build the layer that lets a retailer sell advertising inside their own site to the brands they already stock, and lets a publisher prove what their audience is worth in transactions rather than impressions.
There is no ad server today. No auction. No campaign model. No advertiser interface. It is a blank page, and you get to decide what goes on it.
First, the thing only we can build: measurement.
Impression, view, engagement, product interaction, add to cart, checkout, purchase. Client-side and server-side. One canonical event model, identity resolution, consent, and reporting a brand manager can read without an analyst.
Then the thing that makes money: on-site retail media. Sponsored placements on inventory we already control—timeline, shoppable video, live—sold to brands the retailer already stocks. No auction required. No ad server required.
After that, connectivity. Campaign and audience APIs into external buying platforms, and eventually programmatic supply through Google Ad Manager and Prebid.
We expect to rent more of the advertising stack than we build. You'll help decide which parts.
You won't be expected to know advertising technology on day one. You will be expected to form an opinion, test it cheaply, and change it when the evidence says so.
You're an engineer who thinks in products.
You've shipped data systems that carried real volume, not prototypes. You're comfortable with Node.js and TypeScript, strong SQL, and event pipelines—BigQuery and Pub/Sub, or close enough that you'll be fluent in a week.
You know what's actually buildable, how long it will take, and when to simplify scope to get something real into production faster. That last part is the job, not a compromise.
You care about the product and not only the code—which means you'll push back when what you've been asked for doesn't make sense.
You can sit with a retailer's analytics lead, work out what they actually need to see, and go build it without a product manager in between.
And you're honest about numbers. In advertising, almost everyone's numbers are slightly wrong and most people don't say so. We'd rather be the ones who can explain the difference.
Terrific is AI-native by default.
We don't treat AI as a separate initiative or an occasional productivity tool. We use it as part of how we investigate, build, document and improve our work.
In this role, you'll have the freedom to use AI to:
We're interested in what you can build with AI—not whether you use a particular tool.
Use it to move significantly faster. Don't outsource your judgment to it.
A strong advantage:
You do not need to have built an SSP, and you don't need ad-operations history. If you've built serious data products and you can hold a product argument, the advertising domain is learnable and we'll teach it.
No take-home project. About four hours of your time in total.
We'll tell you what we're assessing before each round, and we'll give you feedback either way.
In your first months, you will:
Success is not shipping every item on a roadmap.
Success is a retailer being able to say "advertising on my own site produced this much revenue"—and prove it.
Most engineering roles in advertising begin with a ticket and end when the ticket is closed.
This one begins with a blank page.
You'll decide what the product is, not only how it gets implemented. You'll work directly with customers, with the people making commercial decisions, and inside a company that already has the data—just not yet the product.
The advertising industry spends enormous effort modeling what Terrific observes directly.
You'll be the one who turns the observation into revenue.
Terrific operates as a global company, and English is our primary business language. Interviews, documentation and regular internal communication will be conducted in English.
\nRequired:
Be part of the change that’s quietly reshaping how people discover and buy online.
Passionate about social media and e-commerce? Come build what’s next at Terrific.
Please note: We review every application carefully. While we may not be able to respond to everyone, we will reach out to candidates whose experience aligns with what we’re building.
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