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Mid-level role, metro location, and common Data Engineer title increase applicant competition.
Core data engineering skills transfer across industries, procurement/scientific dataset experience adds moderate specialization.
Explicit 5-year requirement plus mandatory dbt, Databricks, Spark, cloud and CI/CD skills enforce strict filters.
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Design, implement, and maintain efficient, modular data pipelines and ETL processes to ensure seamless data integration and transformation.
Manage pipeline performance through regular maintenance, upgrades, automated data quality testing, and troubleshooting to deliver reliable data solutions.
Collaborate with cross-functional teams including data modelers, scientists, and subject matter experts to drive impactful data projects and adherence to data governance and standards.
Minimum 5 years of experience in creating and maintaining data pipelines.
Expert knowledge in dbt, SQL, Python, Spark (PySpark), Git, Azure DevOps, and Databricks.
Proven expertise in cloud engineering (AWS or Azure), with mandatory experience deploying CI/CD pipelines; infrastructure-as-code experience is a plus.
Work Experience Required: 5 years in relevant data engineering roles.
Experienced in scientific datasets, particularly in cheminformatics, bioinformatics, or microbiome data, with familiarity of FAIR data principles and scientific data governance.
Strong focus on development, deployment automation, and consistent application of best coding, testing, and documentation practices for scalable solutions.
Collaborative operator adept at working across teams and subject matter experts to deliver integrated, high-quality data infrastructure in a cloud environment.