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Mid-level generalist data role in a metro with broad AWS/Databricks needs and strong employer brand increases competition.
Core data engineering skills are widely transferable across industries; domain (life sciences) is only a plus.
Explicit 5–8 years requirement plus SME-level Databricks and specific AWS tooling makes filters stringent.
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Design, build, and maintain scalable ETL pipelines and data products, ensuring high-quality, analytic-ready data solutions.
Own end-to-end data infrastructure, collaborate with data teams, and implement security and quality validations on data.
Serve as Subject Matter Expert on data & analytics solutions, mentor team members, and drive initiatives using Agile/Product based approaches.
5-8 years of hands-on experience in data engineering or software development, preferably with cloud data solutions.
Expertise in Databricks, AWS data engineering services (Glue, Redshift, Athena), CloudFormation, GitHub workflows, and building APIs using AWS.
Strong programming skills in Python, PySpark, Scala, and experience with SQL databases like MySQL or PostgreSQL.
Work Experience Required: 5+ years in data engineering or software development.
Experienced with full data lifecycle technologies including data lakehouses, data quality, master data management, analytics/AI ML in cloud environments.
Comfortable working in fast-paced, agile, product-oriented teams collaborating globally.
Ability to analyze complex environments and lead process improvements to deliver scalable data solutions end-to-end.