Product Analytics Engineer

OOONO; København; On-site; DKK 700,000 - 1,000,000; Full time

Benefits offered by this job

Monthly social events

Table tennis & foosball at office

Casual, flat culture

Job summary

OOONO in Copenhagen is seeking a Product Analytics Engineer who will shape product metrics and data models, owning product definitions and driving data quality across millions of events. You will collaborate with PMs, engineers, and data scientists to ensure reliable metrics and actionable insights.

You’ll own dbt models, surface KPIs through semantic layers, and push for a single source of truth while embracing modern tooling and LLM-assisted analytics as the product evolves.

Qualifications

• Product analytics mindset with end-to-end ownership
• Strong SQL and data modeling fundamentals
• Experience with dbt and data warehousing
• Ability to define and defend product metrics and KPIs

Responsibilities

• Write and maintain product-side dbt models for events and signals.
• Own product metric definitions and surface them through semantic layers.
• Drive the one source of truth by reconciling definitions and retiring duplicates.
• Define and validate tracking with engineers before release and monitor data quality post-launch.
• Analyze cross-feature trends and bring insights into roadmap discussions.
• Collaborate with PMs, software/hardware engineers, and data scientists to ensure data quality.

Job description

If you’re the type of analytics engineer that prefers to have a list or backlog handed over, this is not the job for you.

But if you’re the type that wants a seat in the room where product decisions get made, you’ve come to the right place.

We’re looking for a Product Analytics Engineer who wants to take the torch and show the way. Nobody here hands you a backlog. You decide what's worth measuring on a device that’s used by millions of drivers every day. That means that when our leadership asks whether the last release actually made anyone’s drive better or safer, the number they're looking at is one you defined.

You'd join our Product Analytics & Insights team, reporting to our Head of Product Analytics & Insights. Your closest teammates are two Customer Insights Specialists, who bring the qualitative side (interviews, surveys, the voice of our drivers), and our commercial‑side analytics engineer, who holds the engineering standard for data across the company. You'll sit right next to our data science and ML team, who build the algorithms behind the warnings our drivers rely on. Small enough that everyone knows what everyone else is working on, big enough that you're not the only one losing sleep over a broken pipeline.

What you'll be doing

• Write and maintain the product-side dbt models (billions of device and app events, late-arriving and occasionally lying), shared with our commercial‑side analytics engineer: tested, documented, reviewed like any other code, and with an eye on what they cost to run, not just whether they run
• Own the product metric definitions, including the KPIs our product teams stand behind in front of leadership every week, and surface them through our semantic layer, Steep
• Keep pushing for one source of truth: reconciling definitions, retiring duplicates, and agreeing with the data team who owns which models
• Set the tracking standard and teach it: help PMs decide what they need to know before a sprint starts, define and validate tracking with engineers before it ships, and catch it when a release quietly breaks it. The goal is that tests catch bad data long before it reaches you
• Go deeper where self‑service stops: post‑launch evaluation, cross-feature trends, and the analysis nobody knew to ask for. Bring it into roadmap discussions early, not after the decision’s made
• Help shape how we do product analytics as we grow, including whether and which product analytics tool earns its place
• Work alongside coding agents daily, keep the docs and context they rely on in good shape, and be the person who spots a plausible‑looking wrong number

We imagine you’re someone who

• Has owned product data models end to end: designed, in production, accountable when a number turned out wrong, and wrote the test that made sure it couldn't happen twice
• Is close enough to the business to argue about what a number means, not only how it’s computed, and checks for duplicates, join fanout and timestamp bugs before telling a story about a strange one
• Has strong data engineering fundamentals: dbt, advanced SQL, solid data modelling, comfortable in Snowflake
• Has worked with app or device event data across releases: app versions, iOS vs Android, offline buffering, late or re‑sent events
• Can design event tracking, define the schema before it’s built, check it actually fires, and tell engineers clearly and with evidence when it doesn’t. Even better, enjoys teaching PMs and engineers to get it right themselves
• Enjoys working across disciplines - PMs, software and hardware engineers, designers, data engineers. Most of the good work here sits between teams, not inside one
• Is curious and agentic, and has built something with LLMs, not just used one
• Documents what they build, so the next person (or the next agent) can pick it up without you in the room

How is it working at OOONO?

We're glad we asked. It's a nice working place. As in nice-nice. We have a relaxed, informal atmosphere and a flat hierarchy. Whenever you need a break from your important work stuff, you can get beat in table tennis, foosball, or just smalltalk about the weather by the coffee machine. We have monthly social events where we do anything from go-karting to going to festivals.

And when we're not social? Then we're pretty damn ambitious. We don't like conservative targets or linear growth. We're all about the extraordinary. This has led OOONO to become one of the fastest-growing tech scaleups and surprise-surprise: we have no plans of slowing down. It's a place to grow, thrive, and take the next step in your career.