Data Analyst

Project

The project develops a large-scale media intelligence platform that analyzes consumer attention and engagement across multiple screens in real time. It uses consented data to provide audience insights, measurement, and analytics solutions for the media and advertising industries.

Responsibilities

• Own analytical projects end to end, from ambiguous stakeholder questions through scoping and analysis to actionable business recommendations.
• Analyze complex, large-scale datasets to identify trends in viewership and audience behavior and quantify their commercial value.
• Document methodologies and assumptions clearly so results are reproducible and can be challenged.
• Measure and report data coverage, consistency, and quality across viewership, web, and third-party entity data; escalate material gaps to Data Science and Data Engineering.
• Work persistently with new and messy datasets, and clearly identify when data cannot support a requested conclusion.
• Define and maintain product and business audience metrics with cross-functional stakeholders, ensuring consistent metric definitions.
• Support experiment design and readouts for segment and targeting changes, interpreting signal versus noise appropriately.
• Build and maintain dashboards that enable stakeholders to answer routine questions independently.
• Write production-quality, documented SQL and Python for recurring analyses; version code in Git and participate in code reviews.
• Communicate data-driven results and recommendations to technical and non-technical audiences, including commercial teams and senior stakeholders.
• Collaborate with Data Science, Data Engineering, Product, and commercial stakeholders to translate business questions into analytical specifications and support data models.
• Participate in cross-functional design reviews and working groups.

Must-haves

• Bachelor’s degree in Mathematics, Statistics, Computer Science, Economics, or a similar quantitative field; equivalent practical experience is also accepted.
• 3+ years of professional experience in analytics, insights, or product analytics, with independent ownership of analytical projects.
• Advanced SQL for extraction, transformation, and analysis of large datasets, including window functions, complex joins, and query optimization.
• Ability to query and aggregate billion-row datasets in a distributed engine such as Spark or Databricks.
• Strong Python for statistical analysis of large datasets, with clean, documented, reproducible code.
• Solid applied statistics knowledge: distributions, sampling, hypothesis testing, and assessing whether estimates and differences are trustworthy.
• Experience building data visualizations and dashboards for audiences with different levels of technical knowledge.
• Git and collaborative code review practices.
• Upper-Intermediate English or higher.

Nice-to-haves

• A/B testing or incrementality measurement experience in marketing or media.
• Familiarity with knowledge graphs, taxonomies, entity hierarchies, or product hierarchies.
• Analytics engineering experience with dbt, semantic layers, or data modeling for self-service analytics.
• Exposure to ML concepts such as propensity, lookalike, or classification models.
• Knowledge of privacy-preserving data practices, including GDPR, consent, and data clean rooms.
• Familiarity with TV measurement vendors, including Nielsen, Comscore, iSpot, TiVo, and Kinetiq.
• Effective use of AI assistants in an analytical workflow.

Benefits

• Opportunity to change projects and develop expertise in a business domain of interest.
• Mentoring and onboarding support.
• Opportunity to earn an additional annual bonus of up to EUR 1,000 per month, depending on expertise and participation in company activities.
• Access to a corporate training portal.
• Certification compensation, including AWS and PMP.
• Referral program.
• Private health insurance and sports compensation, depending on employment type.