Define analytics including the set up of analytics environment and support in defining data governance, including data lifecycle management
ETL process implementation (telemetry based and stationary test data)
Development of large scale data processing engines, real-time search and analytics based on time series data
Working together with data scientist on development of complex on-premise, hybrid and cloud solution architectures
Design, construct and maintain large-scale (time-series) data processing systems (aggregation, transformation, cleansing, enrichment)
Scale out of design algorithms via implementation in cloud-native applications
Ensure know-how development and transfer as well as continuous improvement in the field of data engineering
Engineering degree (BSc, MSc, MEng or PhD) in computer science, computer science/telematics, mathematics/statistics or equivalent technical degree.
At least 3 years of professional experience and knowledge in the following areas:
Python (mainly in the PyData stack (Pandas/Numpy and related packages).
High level programming languages (C#/C++/Java)
Scalable processing environments like Python Dask (or PySpark)
Linux and scripting languages (Bash Scripts)
Database technologies (SQL/OLAP and non-SQL)
Containerization and orchestration of containerized services (Kubernetes).
Proven experience in the use of Cloud technologies (Azure, AWS, GCP) for industrial applications and in the setup of CI/CD data pipelines (including deployment and monitoring of ML-based models) in cloud environments
A working knowledge of vehicle architectures, communication and components
Fluent speaking and excellent writing skills in English
Willingness and ability to interface constructively with a global team
Readiness for business trips in course of development projects