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Mid-level data engineering role in metro with broad cloud/Databricks requirements increases applicant competition.
Core data engineering skills transfer across industries; life-sciences experience is only a preferred plus.
Explicit 5-8 years and mandatory Databricks/AWS stack create strict technical filters.
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Design, build, and maintain ETL pipelines and data products ensuring high-quality and analytics-ready data solutions.
Own end-to-end data architecture and solution delivery including data models, storage optimization, and security protocols.
Collaborate with cross-functional teams to align data infrastructure with analytics and data science needs; act as subject matter expert on data & analytics solutions.
5-8 years of hands-on experience in data engineering with expertise in cloud environments, especially AWS.
Strong expertise in Databricks, AWS Glue services, CloudFormation, GitHub workflows integration, and related AWS data engineering ecosystem tools.
Proficiency in programming languages such as Python, PySpark, Scala, SQL, and experience with AWS data technologies like Redshift, Athena, Lake Formation.
Work Experience Required: 5+ years in data engineering or software development.
Experience working in Agile, product-based teams with global collaboration, preferably in Lifesciences R&D domain.
Demonstrated ability to lead complex data initiatives from analysis through execution with minimal oversight in fast-paced environments.
Strong strategic mindset with expertise in evolving data platforms, API development, and driving improvements in data processes and infrastructure.