Senior Scientific Data Engineer

Eurofins•Stafford, TX

About The Position

Eurofins Scientific is an international life sciences company that provides analytical testing services across multiple industries. This role focuses on interpreting and processing complex chemical data, including chromatograms and environmental testing results. The Senior Scientific Data Engineer designs, builds, and maintains data pipelines and infrastructure that support machine-learning model training, deployment, and analysis workflows, working closely with Machine Learning Engineers and chemists.

Requirements

Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field, or equivalent practical experience.
Expert-level proficiency in Python, including Pandas and NumPy, and familiarity with data engineering libraries.
Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP), including computing, storage, and serverless services; Azure is preferred.
Proven experience building and managing data workflows with an orchestration tool such as Apache Airflow, Prefect, or Dagster.
Strong SQL knowledge and experience with relational and NoSQL databases.
Proficiency with Git and standard DevOps practices.
Prior experience handling and processing complex, large-volume scientific data, such as mass spectrometry, chromatography, or LIMS/ELN integration.
Familiarity with MLOps platforms and tools such as Azure ML Studio, MLflow, Kubeflow, or SageMaker.
Basic understanding of analytical chemistry concepts, including chromatography fundamentals such as retention time and peak integration, and relevant file formats.
Authorization to work in the United States without restriction or sponsorship.
Professional working proficiency in English, including the ability to read, write, and speak in English.
Strong analytical and problem-solving skills focused on delivering high-quality, reproducible data solutions.
Excellent verbal and written communication skills, with the ability to connect technical infrastructure, data science models, and chemical applications.

Responsibilities

Design, build, and manage scalable, reliable ETL/ELT pipelines to ingest, clean, transform, and store raw chemistry data, including CSV, JSON, and proprietary instrument formats.
Develop optimized data models and manage a data warehouse or data lake to support querying and ML feature engineering for complex datasets, including time-series and chromatogram spectral data.
Collaborate with Machine Learning Engineers to containerize and deploy ML models and build automated model retraining and monitoring pipelines.
Implement data quality checks, validation, and monitoring to ensure the integrity and reproducibility of chemical experiment data used for ML.
Develop internal tools and APIs to facilitate data access for Machine Learning Engineers and provide standardized interfaces for data submission from chemistry lab systems.