Senior Data Engineer
Core
Design, build, and maintain robust data pipelines and infrastructure to support ML model training, deployment, and analysis workflows for complex chemical data (chromatograms, environmental testing results).
Role type
Senior Data Engineer (Scientific Data & MLOps)
Builds
Scalable ETL/ELT pipelines, data warehouses/lakes, automated model retraining and monitoring pipelines, and internal data access tools/APIs.
Domain
Life Sciences / Analytical Chemistry / Cloud Infrastructure
Deliverable
production ML models | infrastructure
Required skills
Python (Pandas, NumPy), Cloud Infrastructure (AWS/Azure/GCP), Data Orchestration (Airflow/Prefect/Dagster), SQL & NoSQL, Git/DevOps, MLOps platforms, Scientific Data Processing, Analytical Chemistry concepts
Preferred skills
Experience with mass spectrometry, chromatography, LIMS/ELN integration, Azure ML Studio, MLflow, Kubeflow, Sagemaker
Technologies
Python, Pandas, NumPy, Azure Data Lake Storage, Azure Functions, Apache Airflow, PostgreSQL, Git, Azure ML Studio, MLflow, Kubeflow, Sagemaker
Responsibilities
Design and manage scalable ETL/ELT pipelines for raw chemistry data; Develop optimized data models and manage data warehouses/lakes for ML feature engineering; Collaborate with MLE to containerize and deploy ML models; Implement data quality checks and monitoring; Develop internal tools and APIs for data access and submission.
Seniority
Senior, hands-on IC
