Analytics Engineer

GoodHabitz is building an activation-first product strategy, and the Product Data function is being built from the ground up. As Analytics Engineer, you’ll be the discipline’s technical backbone, establishing governed, trustworthy models across the data estate and setting the standards the function will run on. You’ll work hands-on in Databricks with dbt, partnering closely with the data engineer on modelling and with product and business stakeholders on what to measure and why. This role reports to the Product Data Lead. Role responsibilities:

• Build trust in the data estate. Assess the Databricks estate, define trust levels, and propose owners for each source and table.

• Model with dbt. Build and version silver-to-gold transformations with dimensional modelling and a semantic layer, designing point-in-time dimensions that preserve history.

• Own the event taxonomy. Design and roll out one event taxonomy and naming convention, partnering with engineering to govern it as a shared contract.

• Define shared metrics. Own the metric catalog and sign-off process, and build activation, engagement and retention models leadership can steer by.

• Enable self-serve. Build semantic models and documentation that let product and business teams answer their own questions.

• Enforce quality. Set data quality standards, build sign-off into a Definition of Done gate, and monitor instrumentation to catch issues early.

• Production-level experience with SQL and dbt (or equivalent), with Git as a working habit

• Experience with dimensional modelling and semantic layers

• Experience building a tracking plan or event taxonomy in partnership with engineering

• A track record of building enforcement processes such as data contracts, CI validation or DoD gates, not just writing standards

• Comfort translating ambiguous product and business questions into clear, durable measurement definitions

• Experience with Databricks/Unity Catalog, B2B SaaS multi-surface products, or data observability tooling is a plus

Who we are: Welcome to GoodHabitz, one of the fastest-growing international EdTech companies in Europe. Our mission? To make learning as accessible, engaging, and fun as binge-watching your favorite series, scrolling through your feed, or watching your team score a winning goal. We create unique online training experiences available in 10 countries, all produced in-house at the GoodHabitz Studios. That’s exactly where you’ll land: a multidisciplinary team of educational designers, writers, graphic designers, video creatives, and creative producers who turn learning science into experiences people actually want to use. What we offer: Become part of the leader online training company in the European market, the benchmark in our sector, and help organisations thrive while you grow professionally at a trailblazing company with unstoppable momentum on an exciting growth journey even as others in the industry face challenges.

• Competitive salary and role-specific performance bonus because we value your contributions and reward your hard work.

• ️ Paid time off - 25 days holiday.

• Travel budget.

• Flexible work & tools - work in a supportive environment with the comforts you need, plus a laptop.

• Growth & development - unlimited access to GoodHabitz resources and MyAcademy to fuel your personal and professional growth.

• Mental coaching - support from our partner, to keep your mind in top shape.

• Diverse & inclusive teams - work with colleagues from across Europe, bringing different cultures, perspectives, and ideas together.

• Themed events & team-building - from creativity workshops to vitality socials, our events are full of energy and fun surprises.

• Annual Do-Good Day - a fully paid day to do volunteer work, alone or with your team, supporting a cause you care about.

• ️ Pension & insurance - disability and pension coverage for your long-term security.

• You’d rather work within an established data estate than build governance and standards from scratch

• You treat a standard as finished once it’s written, rather than building the mechanism that makes it stick

• You prefer one-off fixes over designing models built for reuse across a fragmented estate

• You’re not comfortable being the first to define “how we do this here” and holding the line once it’s set