Build scalable data architectures and pipelines for API, batch, event, and streaming sources. Develop aggregation, preprocessing, governance, security, and lifecycle processes; support cloud OLTP and OLAP workloads; and integrate machine learning and analytics components, including MLOps. Collaborate with data science and application development teams to create, deploy, monitor, and maintain secure data solutions using AWS, containerization, infrastructure as code, and CI/CD.
This is a remote position.
A Data Engineer is sought to join an expanding Digital Health Start-Up. If you are passionate about building and scaling world-class MedTech products, solving real-world problems and making a difference in patients’ lives this could be the role for you!
The role can be based in Munich or on a fully remote basis within Europe.
The successful candidate will work for a business committed to empowering people living with chronic conditions to manage their own health and lead a quality life by harnessing the power of IoT, sensor, and AI technologies to ensure they get access to the intervention and personalised care they need.
- Develop scalable data management and data processing architectures.
- Manage data acquisition from API, batch, event or streaming sources.
- Develop processes for data aggregation.
- Design and develop data pre- and post-processing stages.
- Plan and design for data governance, security, provenance and the over-all data lifecycle.
- Leverage best-in-class cloud technologies to cater for OLTP and OLAP business needs.
- Integrate ML models and Analytic components into the workflows (including MLOps).
- Work closely with Data Science and Application Development teams in an agile development process.
Requirements
- 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.).
- Experience with Model Deployment / ML Ops is desirable. Edge-based inference is also of interest.
- Experience with AWS (Fargate, RDS, EC2, SageMaker, Timestream, EMR, Kinesis, MWAA, etc.), Docker, IaC (Terraform), CI/CD, monitoring and related tooling.
- Experience with Time-Series Data is a bonus.
- 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)
Benefits
The role offers a competitive salary and most importantly the chance to be a central player in the future of healthcare.
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