Data Engineer (English speaking)(M/F/D)

First insights

Data engineering is your passion and you love to make sure that data can be turned into valuable assets? You are keen on creating data products that enable teams and organisations to increase their productivity? You have solid experience in designing and maintaining data architectures? You have an engineering mindset and love to analyze complex datasets? You love to work in an agile environment and deliver in short iterations? If this sounds interesting to you, then it is YOU we need in our team!

That’s what it’s about

• Design, develop, optimize, and maintain data architecture.
• Design and maintain ingestion of multiple data sources.
• Analyze, manipulate, transform and process large and complex datasets.
• Enable training and running machine learning models.
• Building real-time data pipelines.
• Help our customers to become cloud-native and data-driven companies.
• Support your team with active knowledge transfer.
• Be part of and influence the introduction of new tools, methodologies and techniques.
• Work in an agile environment and cross-functional team.

This is you

At heart, you are a passionate team player who respects the opinions of his colleagues, as:

• You know how to be the best team player.
• You have an eye for details and ace in documenting your work.
• You base your decisions on metrics.
• You are very structured, and you set the benchmark for quality.
• You are open to new technologies.
• You have at minimum 5 years of experience as a Data Engineer.
• You have at minimum 3 years of experience in either Python or Scala and SQL.
• You have a bachelor's in computer science, data science, or data engineering, or you have a relevant subject such as mathematics or physics.
• You have experience in semantic modelling of complex data landscapes and are familiar with concepts of Data Lake, Data Warehouse, Data Vault, Data Mart.
• You have a deep understanding of various data stores, both structured and unstructured, and their capabilities (i.e. distributed filesystems, SQL and NoSQL data stores).
• You know exactly how to structure data pipelines for reliability, scalability and optimal performance.
• You are comfortable working with analytics processing engines (i.e. Spark, Flink).
• You have worked with many different storage formats and know when to use which (i.e. JSON, Parquet, ORC).
• You speak fluent English (maybe even a bit German).

Bonus experience (nice to have):

• ML Engineering & MLOps experience, including deploying, tracking, and monitoring models in production using MLFlow, Kubeflow, TensorFlow Serving, or similar tools.
• Experience with cloud technologies such (Azure) Databricks, Fabric, Snowflake, AWS Athena or Google BigQuery
• Experience building real-time data pipelines using tools like Azure Stream Analytics, Amazon Kinesis, Google Cloud Dataflow, Kafka, or RabbitMQ.
• Familiarity with CI/CD for data pipelines and ML models, including tools such as GitHub Actions, Jenkins, or Airflow.