• A trusted communicator who builds relationships across technical and non-technical stakeholders and translates data into decisions

• Self-driven and proactive, but also team-oriented, with sound judgment on when to lean on AI tools for speed and when human oversight and design thinking matter more 

• Comfortable working in ambiguity — organized yet flexible, able to adapt as tools, priorities, and team structures evolve quickly 

• A curious, fast learner with a growth mindset — genuinely energized by new tools, AI-assisted workflows, and continuously raising your own technical bar 

• Based in Stockholm or Munich 

• A degree in a quantitative field (e.g. software engineering, computer science, economics, mathematics, statistics) — if you think you’re a great fit, we want to hear from you! 

• At least 2+ years of relevant experience in fields like Analytics Engineering, Data Engineering, Data Analytics, Data Science, or Software Development.  

• Strong SQL skills, with hands-on experience building and testing data models in dbt on a cloud warehouse, and an understanding of dimensional data modeling (star schemas, facts and dimensions) 

• Solid grasp of version control with Git, including CI/CD workflows for data pipelines 

• Familiarity with Python for data transformation, automation, and light pipeline work 

• Fluent in written and spoken English (business level) 

• Comfort working with AI copilots and AI-assisted analytics tools (e.g. Copilot-style code/doc generation, AI BI assistants) while maintaining human oversight on data quality and design 

• Bonus: Experience with, or strong interest in, semantic layers and metric governance — ensuring a single, trusted definition of key business metrics that both humans and AI tools can rely on 

• Transform raw data into clean, usable datasets for analytics and business intelligence, and AI use cases. 

• Collaborate with stakeholders to define and maintain a semantic layer and single source of truth for key business metrics. 

• Ensure data quality and consistency across teams and tools. 

• Build and maintain robust, well-documented data models using Snowflake and dbt. 

Governance & Enablement 

• Provide compliant, user-friendly access to data across various tools (e.g. Power BI, Excel, or AI-powered BI assistants). 

• Empower users of all technical levels — including AI agents — to find, understand, and securely query the data they need. 

• Own the data access lifecycle end-to-end, balancing self-service efficiency with security, privacy, and compliance requirements. 

• Maintain clear documentation and metadata (data catalog, definitions, lineage, ownership) so both people and AI tools can trust and correctly interpret the data 

Optimization & Agile Delivery 

• Continuously evaluate and improve data models for performance and scalability. 

• Contribute to agile ways of working, participating in planning, refinement, and delivery cycles. 

• Stay current with emerging tools and practices in the data stack (e.g. orchestration, observability, AI-assisted development) and help the team adopt what's genuinely useful.