Jobbörse

Data Analytics Team Lead

Veröffentlicht am 24. Sept. 2026

Im Job Fit analysieren

YAZIO

Berlin

EnglischLeadDeutsch nicht nötig

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Job Fit

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Gehalt (Schätzung)

Grobe Schätzung: 44.000 €–75.000 €/Jahr

Über diese Stelle

HEY THERE

You've spent the last few years as the analyst sitting alongside a mobile squad—close to the tracking, the experiments and the numbers behind their decisions. You've also been leading a small team as well for a while now, and you don't want to give up either half.

Good, because this role doesn't ask you to. You'll lead our squad analysts, and you'll also be the hands-on analyst for Monetization. Roughly half your week in each role, at least at the start. Stepping up to lead here doesn't mean handing the interesting work to someone else.

You'll be building solid data products, helping our Monetization team optimize conversion and revenue, but you'll also be responsible for ensuring that our product and engineering teams own the quality of the data they produce. Building this culture is a big part of what we're hiring for.

Let's shape the future of nutrition together and make Yazio the most successful nutrition app worldwide!

This role is 100% remote and open to candidates based in Germany, Spain, Portugal or the UK.

YOUR MISSION

Our product generates billions of data points every month, and our development teams run hundreds of experiments a year. In your first year, you'll:

Lead a small team of analysts embedded in our product squads. They're the ones measuring the impact of what engineering builds and helping figure out what comes next

Be the analyst for Monetization yourself. You’ll handle tracking for new features, the experiments behind our conversion funnels and the reporting the squad runs on

Set the example for how the work gets done. You'll write less production code than the analysts in your team, and the way you solve problems is what they'll pick up

Set the data quality bar: what gets tested when data comes in, what gets tested once it's transformed, what gets tested in the reporting layer and what anomaly detection catches before a stakeholder spots it

Move that responsibility upstream to the teams producing the data. Expect pushback from people who haven't had to think about it before

Own the path from raw event data to curated, ready-to-use datasets in our self-service BI tool

Take on the strategy side of squad analytics: how PMs and engineering managers use data when they make decisions

You'll feel at home here if:

You've been the analyst inside a mobile product squad. Event tracking, funnel and user journey analysis, experiments. You plan around release cycles and handle old app versions as a matter of course
You've managed analysts without stepping away from the work yourself
Experimentation is where you're strongest. You’ve run plenty of A/B tests, and you can talk through bandits, quasi-experiments and where Bayesian and frequentist approaches part ways
You've built a data quality regime. You've held a number back because it didn't reconcile, then gone and fixed whatever let it through
You write SQL all day and Python when you need it, and dbt is how you transform data
When you take an analysis to a PM, it comes with what you'd do next. And when a number turns out to be wrong, you say so and change the process that produced it

Experience with subscription businesses, including the mobile gateway SDKs (App Store, Google Play)
A strong statistics background
Dagster or Airflow for scheduling
Data catalogues like DataHub

Here's what we run in production:

Warehouse: Snowflake
Transformation & orchestration: dbt, Dagster
Experimentation: GrowthBook
Self-service analytics: Omni
ML workflows: Prefect
GenAI: Claude

If you've worked with a cloud warehouse, dbt and an experimentation platform in some form, the patterns transfer. AI is part of how we work here: writing and checking code, summarizing data and keeping documentation current. We'd want you to be critical about what it gives back.

An exciting product with millions of users in over 150 countries, driven by our mission to transform the world through healthy eating
A remote-first setup: work 100% from home, join a co-working space, or work abroad for several weeks per year
30 vacation days to relax and recharge, plus 5 child sick days if your little one needs you
A focus-driven culture that values efficiency over long hours - no overtime expectations, and if it happens, you get that time back!
A high-impact environment where your work drives real change - short decision-making processes and no micromanagement
A team culture where ambition and balance go hand in hand, supported by strong connections - from our fully covered company retreat to team meetups and virtual coffee breaks
Tools and support to do your best work: MacBook, monitor, and €1,000 annual learning budget, plus regular feedback and career development
A truly international team with English as our company language and a shared commitment to growth and well-being - supported by Sunday Supplements and Nilo Health

DIVERSITY AND INCLUSION

At Yazio, we believe that diversity fuels innovation. We are committed to fostering an inclusive environment where everyone, regardless of gender identity, race, religion, age, sexual orientation, or ability, feels valued and empowered. We celebrate different perspectives and actively encourage individuals from all backgrounds to apply.

Excited to take YAZIO to the next level and help people worldwide lead healthier lives? We’d love to hear from you! Please send us:

Your CV in English
Any additional materials that showcase your work (e.g., LinkedIn profile, portfolio)

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Eckdaten der Stelle

Arbeitgeber: YAZIO
Position: Data Analytics Team Lead
Stadt: Berlin
Arbeitsmodell: nicht angegeben
Sprache: Englisch
Erfahrungsstufe: Lead
Veröffentlicht: 24. Sept. 2026
Quelle: Arbeitnow

Bewertung des Unternehmens

Glassdoor 4,8
25 Bewertungen
Auf Glassdoor ansehen
Kununu 4,5
65 Bewertungen
Auf Kununu ansehen

Data Analytics Team Lead

Job Fit

Über diese Stelle

YOUR PROFILE

You'll feel at home here if:

Bonus points for:

Here's what we run in production:

WHY US?

Eckdaten der Stelle

Bewertung des Unternehmens

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