Senior Data Engineer

About the role

Eurofins Scientific is an international life sciences company providing analytical testing services to ensure the safety, authenticity, and accuracy of products across multiple industries. This role focuses on applying Machine Learning (ML) to interpret and process complex chemical data, such as chromatograms and environmental testing results. The Senior Data Engineer will design, build, and maintain data pipelines and infrastructure to support ML model training, deployment, and analysis workflows in collaboration with Machine Learning Engineers and Chemists.

Responsibilities

• 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).
• 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.
• Collaborate with Machine Learning Engineers to containerize and deploy ML models and build automated model retraining and monitoring pipelines.
• Implement robust 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.

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 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.
• Proficiency 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, including the ability to read, write, and speak in English.

Skills

• 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.

Benefits

• Comprehensive medical coverage.
• Life and disability insurance.
• 401(k) with company match.
• Paid holidays and paid time off.
• Dental and vision options.
• Career development support and opportunities.