Key skills
PythonAIMLDeep LearningPyTorchTensorFlowPandasNumPyMLOpsKerasETLAnalyticsAirflowApache AirflowAWSTerraformDockerServerlessFargateEC2LambdaRDSSageMakerKinesisBigTablePostgreSQLMySQLMongoDBRedisAgileCI/CDProduct Management
About this role
BigCircle Ventures is an expanding Digital Health Start-Up seeking a Data Engineer. The successful candidate will develop scalable data management architectures and work closely with Data Science and Application Development teams to leverage IoT, sensor, and AI technologies for improving patient care.
Responsibilities:
- 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)