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Global brand plus mid-level generalist data role and attractive cloud/Databricks skillset increases applicant competition.
Core data engineering skills are broadly transferable across industries; life-sciences domain knowledge is only a plus.
Explicit 5–8 years plus SME-level Databricks, AWS Glue, CloudFormation and related mandatory skills raises filtering strictness.
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Design, build, maintain ETL pipelines and data products with end-to-end ownership, ensuring analytic-ready data solutions.
Optimize data storage and retrieval for scalability and performance while collaborating with data teams to meet business needs.
Serve as Subject Matter Expert for Data & Analytics Solutions, mentor team members, and drive initiatives in an Agile/Product-based environment.
5-8 years experience implementing and operating data capabilities, preferably in cloud (AWS) environment.
Expertise in Databricks, AWS Glue, real-time data ingestion pipelines, CloudFormation, GitHub workflows, and AWS API integrations.
Strong programming skills in Python, PySpark, Scala or similar; experience with SQL and cloud data technologies (AWS, Azure, GCP).
Work Experience Required: 5+ years in data engineering or software development; experience in life sciences domain is a plus but not mandatory.
Experienced in cloud-based data engineering with deep expertise in AWS ecosystem and Databricks as SME level.
Comfortable with ownership mindset driving complex data engineering projects end-to-end in fast-paced, Agile/product-oriented teams.
Skilled in collaborating across data architects, analysts, and scientists, with focus on scalable, high-quality data solution delivery.