Employer description & source details
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) Programming: Expert-level proficiency in Python (including packages like Pandas, NumPy, 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., PostgreSQL) and NoSQL databases. Version Control: Proficient with Git and standard DevOps p
Extracted skills: python, sql, postgresql, aws, azure, gcp, machine learning