Product Data Analyst
Based in GermanyFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years
- Required Skills
- PythonSQLProduct AnalyticsA/B testingBigQuerydbt
Requirements
- 5+ years of experience in product analytics, data analysis, or related roles within B2B SaaS environments.
- Strong experience analyzing user behavior, product funnels, activation metrics, retention drivers, and feature adoption.
- Deep expertise in SQL and strong familiarity with modern data stacks (e.g., BigQuery, dbt, Metabase or equivalent tools).
- Strong understanding of statistical methods, including hypothesis testing, bias detection, and experimental design.
- Proven experience designing, running, and interpreting A/B tests and product experiments.
- Experience defining event tracking schemas and collaborating with engineering teams on instrumentation.
- Strong ability to independently identify high-impact analytical opportunities and drive them to completion.
- Excellent communication skills with the ability to turn complex analysis into clear, decision-ready insights.
- Strong product intuition and ability to influence roadmap decisions with data-backed arguments.
- Experience with behavioral segmentation, cohort analysis, and advanced retention modeling.
Responsibilities
- Act as the dedicated analytics partner to product managers, supporting deep analysis of product usage, user behavior, and feature performance.
- Conduct advanced product analyses across activation, onboarding, adoption, retention, and expansion to identify key behavioral drivers.
- Deliver cross-product and segment-level insights that provide a broader perspective beyond individual product squads.
- Co-develop hypotheses with product managers and design analytical approaches to validate product decisions.
- Influence product roadmap decisions by providing clear, data-backed recommendations on what to build, improve, or deprioritize.
- Design, evaluate, and interpret A/B tests and experiments, ensuring statistical rigor and actionable outcomes.
- Partner with engineering and product teams on event instrumentation, tracking design, and data quality improvements.
- Identify opportunities for deeper analysis proactively, surfacing insights and recommendations even without formal requests.
- Translate complex data findings into clear, structured narratives that support decision-making at all levels.
- Contribute to improving analytical standards, methodologies, and experimentation practices across the product organization.
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