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Mid-level popular data role in a metro with a known global brand and broad requirements.
Skills transferable across industries, life-sciences experience is a preferred but not required plus.
Explicit 5+ years and many mandatory cloud/Databricks/AWS tool requirements increase filtering.
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Develop and maintain ETL/ELT data pipelines for ingestion into data warehouses, focusing on optimization for performance and scalability.
Collaborate with data architects, analysts, and scientists to support data infrastructure that meets their needs, ensuring data quality, integrity, and security.
Drive initiatives independently in an Agile/Product based environment including implementing AWS cloud services and evolving data platform trends.
3-5 years of hands-on experience in data engineering or software development, preferably with cloud (AWS) environment.
Strong expertise in Databricks, AWS services (Glue, Lambda, Redshift, Athena), CloudFormation, AWS API development, and GitHub workflow integration.
Proficiency in programming languages such as Python, PySpark, Scala and SQL with experience in database technologies (MySQL, PostgreSQL, Presto).
Work Experience Required: 5+ years of experience in data engineering or software development.
Experienced with full data lifecycle management including data lakehouses, master/reference data management, data quality, and AI/ML analytics integration.
Comfortable working autonomously in fast-paced, Agile and product-oriented teams with global collaboration, including US and other international sites.
Able to lead process improvements and deliver complex data engineering solutions with strong problem-solving and analytical skills.