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., * 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

What we expect

• 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, Our engineering team is made up of engineers primarily based in our Berlin and Würzburg offices. The team is organised into cross-functional squads aligned to product domains, bringing together backend, frontend, and infrastructure expertise., Our finance team is a small, lean group primarily based in Berlin, covering FP&A, accounting, and commercial finance. The team works closely with leadership and partners directly with departments across the business., Legal is a small, highly collaborative team working across commercial, regulatory, and compliance areas. The team operates globally, partnering closely with teams across the business to support a wide range of initiatives., Legal enables the business to scale internationally while managing risk in a complex regulatory landscape. Their work supports customers across diverse industries-from smart cities and aviation to EV charging and beyond-ensuring we can operate confidently across markets., Our product managers are primarily based in Germany and across Europe, working closely with engineering and design in cross-functional teams aligned to key product domains., You’ll lead product discovery, prioritisation, and delivery-working closely with customers and internal teams to identify opportunities, define solutions, and bring new features to market that solve real-world connectivity challenges at scale.

• Your experience is primarily in data analysis or BI, rather than building and owning production-grade analytics engineering foundations., Be ready to demonstrate your skills through tasks like case studies, presentations, panel discussions, or coding challenges (depending on the role).

Enjoy a generous time-off policy starting at 30 days (location-based) to recharge and focus on what matters most.