Staff Analytics Engineer AI-Powered Analytics

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) 

Your Impact 

• 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 

Your Skills 

• Proven experience designing and operating semantic layers, metrics models, and dimensional models in a modern stack, with production-grade SQL and strong Python 

• Hands-on experience applying LLMs to data and analytics use cases — combining retrieval, structured context, and validation to produce reliable answers — with a clear understanding of prompting, context engineering, and failure modes 

• Experience building trusted data products through testing, documentation, ownership, contracts, and lineage — with pragmatic governance that keeps teams fast 

• An AI-native engineering mindset: you use AI-assisted development tools daily and combine that acceleration with rigorous validation and review 

• Clear communication and product thinking — translating business needs into scalable data solutions and influencing direction across teams 

Nice to have: experience in SaaS, usage-based businesses, telecom, or IoT.