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Eurofins Environment Testing (USA) · 4 hours ago

Data Engineer

Stafford County, VA

Full-time

Onsite

Mid Level

Eurofins Scientific is an international life sciences company that provides analytical testing services to ensure safety and authenticity in various industries. They are seeking a skilled Data Engineer to design and maintain data pipelines and infrastructure to support machine learning model training and analysis workflows.

BiotechnologyManufacturing

No H1B

Responsibilities

Data Pipeline Development: Design, construct, and manage scalable and reliable ETL/ELT pipelines to ingest, clean, transform, and store raw chemistry data (e.g., CSV, JSON, and proprietary instrument formats)

Data Modeling & Warehousing: Develop optimized data models and manage a data warehouse (or data lake) to support fast querying and ML feature engineering on complex datasets, including time-series and spectral data from chromatograms

ML Infrastructure: Collaborate with MLE to containerize and deploy ML models and build automated model retraining and monitoring pipelines

Data Quality & Governance: Implement robust data quality checks, validation, and monitoring to ensure the integrity and reproducibility of chemical experiment data used for ML

Tooling: Develop internal tools and APIs to facilitate data access for MLE and provide standardized interfaces for data submission from chemistry lab systems

Qualification

PythonCloud ComputingData OrchestrationSQLScientific Data ExperienceMLOpsChemistry DomainVersion ControlProblem-SolvingCommunication

Required

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 packages like Pandas, NumPy, and familiarity with data engineering libraries)

Hands-on experience with at least one major cloud provider (AWS, Azure, or GCP, preferred Azure), including services related to computing, storage, and serverless functions (e.g., Azure Data Lake Storage (ADLS), Azure Compute VMs, Azure Functions)

Proven experience building and managing data workflows using an orchestration tool like Apache Airflow, Prefect, or Dagster

Strong knowledge of SQL and experience working with both relational (e.g., PostgreSQL) and NoSQL databases

Proficient with Git and standard DevOps practices

Prior experience handling and processing complex, large-volume scientific data (e.g., mass spectrometry, chromatography, LIMS/ELN integration)

Familiarity with MLOps platforms and tools such as Azure ML Studio, MLflow, Kubeflow, or Sagemaker

Basic understanding of analytical chemistry concepts, such as chromatography fundamentals (e.g., retention time, peak integration) and relevant file formats

Authorization to work in the United States without restriction or sponsorship

Professional working proficiency in English is a requirement, including the ability to read, write and speak in English

Strong analytical and problem-solving skills with a focus on delivering high-quality, reproducible data solutions

Excellent verbal and written communication skills, with the ability to bridge the gap between technical infrastructure, data science models, and chemical applications