Data Engineer / Machine Learning Engineer (w / d / m)

  • codecentric AG
  • Allensbach, Germany
  • 04/11/2019
Full time Data Science Data Analytics Big Data Statistics

Job Description


For us, the employees are in the foreground with their individual goals, ideas and strengths. Thus you proactively shape your career and role. If you can identify with the following position description for the most part, we look forward to receiving your application.

What's waiting for you

  • As a Data / ML Engineer (w / d / m) at codecentric, you take care of all the processes involved in the generation, storage, maintenance, processing, enrichment and transfer of large amounts of data.
  • You ensure that various data sources and sinks can be "tapped" through well-defined interfaces. The developed models should also work on large amounts of data.
  • You are only satisfied if the Recommender or the ML application has been successfully deployed at the customer.
  • In everything you do, you have the scalability, usability, and maintainability of the system in mind.
  • You explain to customers the technical world beyond buzzwords and hype, argue decisions and help clients implement their data strategy.
  • You work closely with data scientists, the customer's departments, and the development team.

You should bring that

  • Professional programming skills in Python, Java or Scala
  • Sound knowledge in one or more of these areas:

    Frameworks for batch and stream processing (eg Apache Spark, Flink, Kafka), experience in working with SQL & NoSQL databases and / or object-based data lakes, machine learning DevOps & deployment (eg dvc, ml-flow, ONNX), Cluster Manager (eg Hadoop YARN, Kubernetes or Mesos) & Automation (eg with Docker or Airflow)

  • Ideally, touchpoints with ML solutions from major cloud providers like AWS Sagemaker, Google ML Engine or Azure ML Service

  • Basic knowledge of known ML / AI frameworks (eg Scikit-Learn, Caffe, TensorFlow or Keras)

  • Knowledge in Python Data Stack (eg Pandas, NumPy, Jupyter Notebook) are welcome but not necessary

  • Data science techniques and problems, such as Time Series Analysis, Natural Language Processing, Computer Vision, Reinforcement Learning and Feature Engineering, are no stranger to you.