Data Engineer
RemoteMunich, Bavaria, Germany
Munich, Bavaria, GermanyRemoteFull TimeStartup
Full TimeStartup
Job Summary
Develop scalable data management and processing architectures for IoT, sensor, and AI-driven MedTech products. Manage data acquisition from API, batch, event, and streaming sources while designing pre- and post-processing stages and aggregation workflows. Plan data governance, security, and lifecycle management using best-in-class cloud technologies to support OLTP and OLAP needs. Integrate ML models and analytic components into workflows, collaborating with Data Science and Application teams in an agile process.
Required Qualifications
- B.Sc., B.Eng. or higher in Computer Science, Computer / Electronic / Systems Engineering, or similar disciplines
- Proven experience as a Data Engineer
- Experienced with structured, semi-structured and unstructured data (e.g., Relational, JSON, Schema-less)
- Experience with creating, cleaning and curating datasets and databases such as: MySQL, PostgreSQL, MongoDB, Redis, Bigtable, time-series databases or similar
- Serverless/distributed processing experience, e.g., Multiprocessing, containers, lambda or similar
- Know-how for scheduling workflows, e.g., DAGs with Apache Airflow
- Accomplished and versed with various ETL approaches
- Exposure to classical and deep learning-based ML methods (e.g., CNNs, DL Auto-encoders, etc.)
- Knowledge and experience of relevant data, analytics, visualization and ML languages and libraries is important (e.g., Julia/Python, Boto3/Apache Airflow, Parquet, SciPy/NumPy, Pandas/Matplotlib, Keras/TensorFlow, PyTorch, etc.)
- Communicating effectively in an interdisciplinary environment (AI/ML, product management, regulatory, clinical)
- Have practical experience with ETL, Data Pipelines and Cloud Deployments
- Experience in design and building data solutions while ensuring confidentiality, integrity, and availability
- A strong engineering interest in ML and data science
- Business proficient in English (spoken and written)
Desired Qualifications
- Experience with Model Deployment / ML Ops is desirable
- Edge-based inference is also of interest
- Experience with Time-Series Data is a bonus
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