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)
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