Data Analyst

Andersen is a global software development company with over 18 years of experience supporting businesses worldwide in their digital transformation. Headquartered in Poland, it operates development centers across Germany, the USA, the UK, Lithuania, Hungary, Kazakhstan, Georgia, Azerbaijan, Cyprus, and the UAE. Andersen offers end-to-end IT services including custom software development, API integration, system architecture, legacy modernization, and long-term support. The company leverages AI, Machine Learning, Data Science, Big Data, and Visual Technologies to create intelligent digital solutions. Andersen has a team of 3,700+ experts and a portfolio including long-term partnerships with Siemens, S&P Global, Ryanair, and others.

• Bachelor’s degree in a quantitative field or equivalent practical experience.
• 3+ years in analytics, insights, or product analytics, independently owning analytical projects.
• Advanced SQL skills (complex joins, window functions, query optimization).
• Experience with billion-row datasets using Spark, Databricks, or similar.
• Strong Python skills for statistical analysis and reproducible workflows.
• Solid applied statistics knowledge, including sampling and hypothesis testing.
• Dashboard/data visualization experience for technical and non-technical audiences.
• Experience with Git and collaborative code review.
• English proficiency: Upper-Intermediate+.

Nice to have:

• A/B testing or incrementality measurement.
• Knowledge graphs, taxonomies, or product/entity hierarchies.
• dbt, semantic layers, or self-service analytics modeling.
• ML concepts such as propensity, lookalike, or classification models.
• GDPR, consent, clean rooms, or privacy-preserving practices.
• TV measurement vendors experience (Nielsen, Comscore, iSpot, TiVo, Kinetiq).
• AI assistants in analytical workflows.

• Own analytical projects end to end, from stakeholder questions and scoping to actionable recommendations.
• Analyse large, complex datasets to identify audience and viewership trends and assess their commercial impact.
• Measure and report data coverage, consistency, and quality; flag material gaps to Data Science and Data Engineering.
• Define and maintain product and business metrics with cross-functional stakeholders.
• Support experiment design and interpret results, separating meaningful signals from noise.
• Build and maintain dashboards for self-service analytics.
• Write production-quality, documented SQL and Python, versioned in Git.
• Clearly document methodologies and assumptions.
• Communicate insights to technical, commercial, and senior stakeholders.
• Collaborate with Data Science, Data Engineering, Product, and commercial teams on analytical requirements and data models.

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