Data Engineer

Eurofins Scientific is an international life sciences company, providing a unique range of analytical testing services to clients across multiple industries, to make life and our environment safer, healthier and more sustainable. From the food you eat, to the water you drink, to the medicines you rely on, Eurofins laboratories work with the biggest companies in the world to ensure the products they supply are safe, their ingredients are authentic, and labelling is accurate.

Eurofins is dedicated to delivering testing services that contribute to the health and safety of society and the planet, and to its corporate responsibility to protect the environment and ensure diversity, equity, and inclusion across the entire network of Eurofins companies.

Our team is at the forefront of applying Machine Learning (ML) to interpret and process complex chemical data, specifically chromatograms and environmental testing results. We're seeking a skilled Data Engineer to design, build, and maintain the robust data pipelines and infrastructure necessary to support our ML model training, deployment, and analysis workflows. The ideal candidate has a strong foundation in data engineering, understands the unique challenges of scientific data, and is eager to work closely with both Machine Learning Engineers (MLE) and Chemists.

Key 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.

Education: Bachelor’s or Master’s degree in Computer Science, Data Engineering, or a related technical field (or equivalent practical experience)

Required Experience:

• Programming:
  Expert-level proficiency in Python (including packages like Pandas, Num Py, and familiarity with data engineering libraries).
• Cloud & Infrastructure:
  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).
• Data Orchestration:
  Proven experience building and managing data workflows using an orchestration tool like Apache Airflow, Prefect, or Dagster.
• Databases:
  Strong knowledge of SQL and experience working with both relational (e.g., Postgre
  
  SQL) and No
  
  SQL databases.
• Version Control:
  Proficient with Git and standard Dev Ops practices
• Scientific Data
  
  Experience:
  
  Prior experience handling and processing complex, large-volume scientific data (e.g., mass spectrometry, chromatography, LIMS/ELN integration).
• MLOps:
  Familiarity with MLOps platforms and tools such as Azure ML Studio, MLflow, Kubeflow, or Sagemaker.
• Chemistry Domain:
  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.

Required General Skills

• Problem-Solving:
  Strong analytical and problem-solving skills with a focus on delivering high-quality, reproducible data solutions.
• Communication:
  Excellent verbal and written communication skills, with the ability to bridge the gap between technical infrastructure, data science models, and chemical…