Senior Data Analyst - Experimentation & A/B Testing (f/m/d)

adjoe builds the technologies behind mobile apps growth and monetization. With our core product Playtime Arcade, we've become the global leader in rewarded advertising, an ad unit built on a simple premise: users earn real in-app rewards for engaging with new apps. The result is one of the most effective value exchanges in adtech, connecting advertisers and publishers with over 770 million users annually.

Investigating 770 Million User Interactions Across a 100TB Data Lake

When a platform serves 770 million users, every optimization from reward mechanics to SDK integrations needs to be grounded in data. The Rewarded Analytics team builds the internal dashboards, alerting systems, and data pipelines that monitor the user funnel, traffic engagement, and reward consistency.

SQL and Python power deep-dive investigations and automated 100 terabyte-sized data analyses queried through Trino. Using Tableau as the main visualization tool, stakeholders have access to real-time insights on the product performance. Airflow orchestrates the pipelines that keep everything up to date. This team provides the data that turns questions into answers: every A/B experiment the product team runs, every anomaly a publisher flags, and every new SDK integration. These answers guide all the business decisions.

Your Mission & Who We Are Looking For:

• Data analyst with a strong technical foundation. You have 5+ years as a Data Analyst and work fluently in both SQL and Python, writing complex queries, big data optimizations, and analysis pipelines to support product and growth initiatives.
• Master of experimentation and A/B testing. You have deep experience designing experiments, calculating sample sizes, managing variance reduction techniques, and analyzing A/B/n tests to ensure statistically sound business decisions.
• Statistically grounded. You can reason confidently about core statistical concepts—hypothesis testing, confidence intervals, p-values, power analysis, and Bayesian vs. Frequentist approaches—and apply them to real-world product decisions.
• Not a stranger to anomaly detection. You have experience working with anomaly detection algorithms and approaches like distribution analysis and cross-population comparison to monitor test guardrail metrics and identify unexpected shifts in experiment data.
• You own the systems, not just your slice. You understand the end-to-end telemetry and feature flagging systems that power experimentation. You can speak to data flows, event tracking pipelines, architecture tradeoffs, and why things were built the way they were.
• Automation-minded across the stack. You think beyond one-off test analyses and build automated experiment reporting dashboards, reusable analytical frameworks, and data visibility solutions. Experience with dbt, Airflow, or experiment platforms (e.g., Optimizely, GrowthBook) is a strong plus.
• Collaborative and business-aware. You work closely with product managers, engineers, and growth leads to translate complex statistical results into clear, actionable recommendations that drive executive decision-making.

🌎 We welcome applications from talent worldwide and provide relocation support to Hamburg, Germany for those ready to join our team.

What’s in It for You?

At adjoe, you’re not here to just close JIRA tickets, you’re helping build the infrastructure behind one of the most impactful platforms in adtech. The systems you work on will reach hundreds of millions of users and power billions of decisions every day.

• Go Big. Own projects with impact on 770M users and push adtech boundaries.
• Move Fast. Ship solutions multiple times a day, learn from results, and keep momentum.
• Be Direct. Solve problems openly and collaborate across teams.
• Thrive Together. Grow with a diverse, global team of people from over 40 different countries that learn from each other.
• Have Fun. Celebrate wins, enjoy daily victories, and bring your energy.

We welcome applications from people who will contribute to the diversity of our company.

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Source: Statistisches Bundesamt (Destatis), table; dl-de/by-2-0; StarIn's calculation