At emnify, we believe the future of analytics is conversational: customers and teams should be able to ask questions in plain language and receive answers that are correct, governed, and explainable. As our Staff Analytics Engineer, you will build the foundation that makes this possible — the semantic layer, metrics, and engineering practices that let both humans and AI systems query our data reliably. AI provides speed; strong foundations provide trust.
You will work closely with data engineering, product, and leadership while remaining hands-on throughout.
Our analytics environment includes:
Lakehouse on S3 with StarRocks as the analytical engine
Fivetran and kafka sync for ingestion, dbt core for transformations, Superset for BI
AWS infrastructure (EKS)
On this foundation, you will build the semantic layer and LLM-powered workflows such as text-to-SQL and RAG.
Our flexible work model includes monthly in-person workshops. Candidates based in Berlin or nearby cities are preferred.
Location: Berlin, Germany (or remote within the EU, with preference for proximity to Berlin)
- Design and own a governed semantic layer that encodes emnify's business logic — SIM lifecycle, churn, usage, unit economics — as reliable, well-documented data products
- Build and productionize AI-powered analytics experiences (text-to-SQL, RAG, analytics assistants), grounded in trusted business definitions rather than AI interpretation of raw data
- Make AI answers trustworthy through evaluation frameworks, regression testing, and monitoring — the biggest risk is not visible failure but confidently incorrect answers
- Raise analytics engineering standards: modeling practices, data quality, governance, mentoring, and design review
- Partner with product and leadership to identify high-value AI analytics opportunities and turn experiments into durable platform capabilities
- Design and own a governed semantic layer that encodes emnify's business logic — SIM lifecycle, churn, usage, unit economics — as reliable, well-documented data products
- Build and productionize AI-powered analytics experiences (text-to-SQL, RAG, analytics assistants), grounded in trusted business definitions rather than AI interpretation of raw data
- Make AI answers trustworthy through evaluation frameworks, regression testing, and monitoring — the biggest risk is not visible failure but confidently incorrect answers
- Raise analytics engineering standards: modeling practices, data quality, governance, mentoring, and design review
- Partner with product and leadership to identify high-value AI analytics opportunities and turn experiments into durable platform capabilities
