Data Engineer Engineering · Munich , Paris · Hybrid

Data Engineer

Make AI Accessible & Sustainable

We usually respond within 2 hours.

Role Description

Unified Data Platform

• Iterate on the design of the general data architecture and develop all the data pipelines.
• Own the selection of technology, solutions, and vendors.
• Handle approximately 15–20 data sources.

Growth Engineering & Trigger-Based Automation

• Design intelligent triggers that automate tasks and anticipate user needs, creating a seamless and proactive user experience.

Product Analytics & ROI Simulators

• Integrate with our products to extract telemetry data, analyze insights, and cross‑reference them with data from other business tools.
• Develop ROI simulators to equip users with data‑driven insights that confirm our value proposition.
• Collaborate with engineering for frontend and backend integration.

Tech, Revenue & Customers Dashboards

• Provide easy‑to‑understand, up‑to‑date dashboards for all team members.
• Partner with engineering to integrate telemetry data and create user‑friendly product usage dashboards.
• Help customers analyze their usage trends in a self‑serve fashion.

Useful Tech

• Hosting provider: AWS
• Infrastructure: Docker, Kubernetes, Terraform
• Datastore: PostgreSQL (RDS)
• Orchestration: GitHub Actions, Kestra or Lambda
• ETL: Airbyte
• Data modelling: dbt or SQLMesh
• Data visualization: Metabase
• Development: Python, SQL, Git

People you will work with

• Engineering team: work with software and ML teams on technical implementation of trackers, simulators, and dashboards relevant to our AI model deployments.
• GTM team: collaborate closely with the GTM team to strategically implement and maintain data platforms for products and customers.
• Communication team: collaborate with the communication team to track distribution channels and strategies for our AI models.

Your Skills

You are a data engineer with a proven track record of tackling complex projects from the ground up. You thrive on quick wins and iterative development, and you are comfortable taking calculated risks and making data‑driven decisions.

We would love to see

Experience

• 3–7 years as a data or analytics engineer with exposure to the modern data stack.
• Proficiency in GitOps & Docker, and in managing version control and environment provisioning entirely as code (Terraform, GitHub Actions…)
• Experience building and implementing a simple yet highly effective data stack from the ground up (events & triggers‑driven, mostly).

Core skills

• Understanding of core tech and business concepts, including AI model deployment pipeline and customer pipeline.
• A constant desire to learn about new technologies and explore their potential applications for data analysis and automation.
• Excellent communication skills (written and verbal) to collaborate effectively with cross‑functional teams.
• Fluency in English.

It is a plus if you have

• Experience with designing automation processes.
• Experience developing ROI simulators and dashboards.
• Experience with telemetry data integration.

Expected Salary

We pay top market rates based on seniority and location, leveraging publicly available data that we share with you during the process. We are also working towards competitive and useful benefit packages.