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Mid-level generalist data role, metro location, and broad skillset create high applicant competition.
Core data engineering skills are transferable, though scientific dataset and FAIR experience moderately favor domain-specific candidates.
Multiple mandatory technologies, 5+ years experience, and leadership responsibilities enforce strict shortlisting.
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Design, implement, and maintain efficient, scalable data pipelines ensuring seamless data ingestion, transformation, and storage.
Manage team and projects, provide technical recommendations, and oversee pipeline performance including automated quality checks and monitoring.
Collaborate closely with data engineers, modelers, scientists, and stakeholders to deliver integrated data solutions aligned with scientific datasets and governance standards.
5 years of experience in creating and maintaining data pipelines.
Expertise in dbt, SQL, Python, Spark (PySpark), Git, Azure DevOps, and Databricks.
Strong experience with cloud engineering (AWS or Azure); CI/CD pipeline deployment is mandatory.
Work Experience Required: 5 years in relevant data engineering roles.
Proven ability to lead technical teams and manage projects with emphasis on data integration and automation pipelines.
Experience working with scientific datasets and familiarity with FAIR data principles and data governance in scientific domains.
Strong operational focus on developing automated, scalable deployment pipelines and ensuring data quality and reliability.