We are building the analytical backbone of a company that believes decisions should be powered by clarity, not guesswork. Our Business Intelligence team doesn’t just build reports we design systems that scale with the business. We care deeply about structure, governance, and long-term thinking.
Here, analysts don’t fight over metric definitions. Leaders don’t debate which number is “right.” AI tools don’t hallucinate their way through raw tables.
We are creating a world where every KPI has a home, every measure has a definition, and every insight is grounded in a trusted semantic foundation. If you think in schemas, care about grain discipline, and see elegance in well-structured systems, you’ll feel at home here.
Your Role:
This is not a dashboard role.
This is not about data ingestion.
This is about ownership.
As our Analytics Engineer, you will design, own, and scale the company’s semantic layer and governed metric framework, the analytical abstraction layer that becomes the single source of truth for company KPIs.
Your work will define how both humans and AI tools (such as Claude) interact with data. You won’t just build models, you will shape how the company understands performance, ensuring every metric is trusted, consistent, and production-grade.
What You'll Be Doing:
- Design and maintain scalable data models and pre-aggregations within our SQL Server data warehouse
- Define and rigorously enforce grain discipline across all fact tables
- Own the metric layer as code-version-controlled in GitLab, reviewed, and deployed through CI/CD pipelines
- Define, formalize, and document core business KPIs across Revenue, Conversion, Retention, Payments, and Finance
- Establish and fully own a governed semantic layer managed by BI
- Build and maintain a structured KPI registry with approved measures, dimensions, and filters
- Design a semantic layer that enables controlled, safe AI access to metrics without exposing raw tables or ambiguous logic
- Ensure AI tools (Claude and others) can consistently and reliably answer analytical questions using governed definitions
- Improve dataset performance through aggregations, partitioning, and incremental refresh strategies
- Eliminate metric duplication and resolve conflicting definitions across reports and teams
- Provide architectural guidance to analysts building on top of the semantic layer
- Design and lead the metric governance lifecycle, how KPIs are requested, reviewed, approved, versioned, and deprecated
- Monitor data freshness and proactively resolve nightly pipeline failures
- Contribute to long-term BI platform and analytics architecture decisions
What You'll Need:
- 4+ years of experience in Analytics Engineering, BI Architecture, or advanced BI development
- Strong expertise in dimensional modelling (Kimball methodology, star schemas, fact/dimension design, grain definition, SCD)
- Hands-on experience with a modern semantic layer tool (e.g.dbt/MetricFlow, Cube, or equivalent) and a strong understanding of metrics-as-code
- Advanced T-SQL skills with performance optimization experience (indexing, partitioning, query tuning)
- Solid Python skills for data transformation, automation, and pipeline scripting
- Proficiency with Git and CI/CD workflows—you treat data models and metrics as production code
- Experience designing and governing metric layers in production environments
- Functional understanding of Power BI semantic models (Tabular, DAX measures, relationships) as a consumption layer
- Interest or experience in LLM-powered analytics (e.g. Claude, AI-assisted querying)
- Strong systems-thinking mindset with structured, architectural discipline
- Ability to communicate and document complex logic clearly and precisely
Nice If You Have:
- Experience enabling AI- or API-driven analytics access (e.g. MCP servers or similar frameworks)
- Experience with natural language or LLM-powered analytics (Text-to-SQL, NL-to-metric systems)
- Familiarity with multiple semantic layer tools (dbtMetricFlow, Cube, AtScale, LookML, etc.)
- Experience optimizing large-scale Power BI datasets in production
- Exposure to subscription or monetization-driven business models
- Experience in fast-growing B2C environments
What You Can Expect On Board:
- Remote-first collaboration
- A role that is not on-call heavy, but with occasional responsibility to respond to critical pipeline failures outside business hours
- High ownership and architectural autonomy, you will own the metric layer end-to-end
- Direct impact on how the entire company, and its AI systems—make decisions
- The opportunity to define how AI interacts with governed business data
- A culture that values clarity, structure, and long-term thinking over quick fixes
- Close collaboration with leadership and cross-functional teams
- The chance to build foundational systems, not incremental dashboards
- Memorable team experiences, from EU meetups to our annual company summer getaway.
If you’re the kind of engineer who obsesses over clean schemas, precise definitions, and scalable systems, this is your opportunity to build something that truly matters.
You won’t just create models, you’ll create trust.
You won’t just define KPIs, you’ll define clarity.
Join us and design the analytical foundation that enables both people and AI to make smarter decisions, every single day.