Quick Summary
We are looking for a full-time Analytics Engineer in-house (m/f/d) to join our motivated global Insights team starting as soon as possible in the Stockholm or Munich office. As an Analytics Engineer,
We are looking for a full-time Analytics Engineer in-house (m/f/d) to join our motivated global Insights team starting as soon as possible in the Stockholm or Munich office.
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A trusted communicator who builds relationships across technical and non-technical stakeholders and translates data into decisions
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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
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Comfortable working in ambiguity — organized yet flexible, able to adapt as tools, priorities, and team structures evolve quickly
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A curious, fast learner with a growth mindset — genuinely energized by new tools, AI-assisted workflows, and continuously raising your own technical bar
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Based in Stockholm or Munich
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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!
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At least 2+ years of relevant experience in fields like Analytics Engineering, Data Engineering, Data Analytics, Data Science, or Software Development.
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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)
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Solid grasp of version control with Git, including CI/CD workflows for data pipelines
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Familiarity with Python for data transformation, automation, and light pipeline work
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Fluent in written and spoken English (business level)
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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
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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
Data Modeling & Quality
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Transform raw data into clean, usable datasets for analytics and business intelligence, and AI use cases.
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Collaborate with stakeholders to define and maintain a semantic layer and single source of truth for key business metrics.
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Ensure data quality and consistency across teams and tools.
- Build and maintain robust, well-documented data models using Snowflake and dbt.
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Provide compliant, user-friendly access to data across various tools (e.g. Power BI, Excel, or AI-powered BI assistants).
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Empower users of all technical levels — including AI agents — to find, understand, and securely query the data they need.
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Own the data access lifecycle end-to-end, balancing self-service efficiency with security, privacy, and compliance requirements.
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Maintain clear documentation and metadata (data catalog, definitions, lineage, ownership) so both people and AI tools can trust and correctly interpret the data
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Continuously evaluate and improve data models for performance and scalability.
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Contribute to agile ways of working, participating in planning, refinement, and delivery cycles.
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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.
Location & Eligibility
Listing Details
- Posted
- June 18, 2025
- First seen
- August 26, 2026
- Last seen
- August 29, 2026
Posting Health
- Days active
- 0
- Repost count
- 0
- Trust Level
- 29%
- Scored at
- August 26, 2026
Signal breakdown
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