Public summary
Join an innovative company in Germany that develops public charging solutions for electric vehicles. The role involves designing and maintaining data architectures and pipelines, enabling AI-ready data, and collaborating across departments to support business goals related to charging, billing, and promotions. The company fosters an international and inclusive culture with opportunities for personal development and flexible work arrangements.
Location and work setup
- Location
- Berlin, Munich
- Remote status
- Hybrid
- German requirement signal
- No German Required Detected
- Detected job language
- English
Responsibilities
Design, develop, optimize, and maintain data architecture and ELT/ETL pipelines supporting business operations such as charging sessions, billing, invoicing, and promotions. Build and manage large-scale batch and streaming pipelines, data warehouse models, and transformation layers that are reliable and performant. Own the semantic data layer to ensure consistent business metrics. Prepare data to be AI-ready, enabling natural language analytics and AI-assisted experiences. Gain deep business domain knowledge and manage integrations between Snowflake, Power BI, SAP, APIs, and other tools. Partner with analysts, data scientists, finance, and product teams to translate requirements into data solutions. Monitor and optimize infrastructure performance and costs.
Qualifications
Minimum 5 years experience as a Data Engineer, preferably in large-scale transactional or subscription businesses such as mobility, energy, fintech, or e-commerce. Proficient in SQL (Snowflake, PostgreSQL), and experienced with dbt or similar frameworks. Strong Python skills for data engineering and pipeline orchestration. Solid understanding of data modeling and semantic layers. Familiarity with cloud data warehouses, APIs, orchestration tools like Airflow, version control (Git), and software engineering practices such as CI/CD. Experience with streaming technologies (Kafka, Azure Service Bus, Spark) and Azure cloud services is desirable. Knowledge of infrastructure as code tools like Terraform or CloudFormation is a plus. Interest or experience with AI and LLM tooling, retrieval-augmented generation (RAG) patterns, or vector stores applied to enterprise data is beneficial. Excellent communication and documentation skills, with the ability to collaborate across technical and non-technical teams.