Data Analyst - Analytics Engineering

• Lead Design Data Architecture – Drive the development of the Data Mesh layer by designing robust data models and building pipelines that integrate into the company's federated data strategy, ensuring scalable and governed data ownership
• Define AI Agentic Workflows – Design and implement AI-powered agentic workflows for data transformation and exploration, accelerating analytical capabilities and enabling self-service for key stakeholders
• Build High-Impact Visualisation Tools – Define key metrics and develop dashboards and reports (using tools like Hex or Looker) that communicate analytical insights effectively
• Drive Decision-Making Through Data – Analyse complex datasets to identify patterns impacting the consumer funnel (Acquisition, Engagement, Retention, Monetisation) and partner with Marketing Managers to maximise ROI
• Partner with Key Stakeholders – Act as a strategic partner for Marketing, Finance, Operations, and Product, bridging the gap between technical data and business goals
• Drive Testing Initiatives – Own the end-to-end testing pipeline, from opportunity identification to experiment design and recommendation delivery

• 5+ years in data-related roles (Data Engineering, Analytics, BI) with at least 3 years of hands-on experience in high-volume or big data environments
• Strong command of SQL, dbt, and Python
• Experience with Git and notebook-based analytics (e.g., Jupyter) is a plus
• Hands-on experience in data modelling, ETL, data governance, and Data Mesh implementation at scale
• Proven ability to work with AI tools, including prompting and context management, or interacting with agents programmatically via APIs
• Comfortable with ambiguity, able to break down complex problems into focused workstreams and deliver evidence-based answers
• Able to independently lead initiatives across diverse business domains and translate technical findings into clear, actionable recommendations
• Solid understanding of consumer lifecycle (Acquisition, Activation, Retention, Churn) and user economics (ARPU, CAC, LTV)

Experience with Git and notebook-based analytics (e.g., Jupyter)

• Private insurance for you and your family
• Psychological support with Serenis
• Mental health workshops
• Stock Option Plan
• Meal vouchers
• Relocation support if you're moving countries
• Professional development programs
• Internal mobility
• Language courses with Preply
• Unlimited PTO
• Hybrid working policy
• Flexible working hours
• Enhanced parental leave
• Additional leave for child sickness