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Mid-level metro data engineer role with general title and broad cloud/Databricks requirements increases competition.
Core data engineering skills are broadly transferable across industries despite a life-sciences preference.
Explicit 5+ years and multiple mandatory cloud/Databricks skills make shortlisting highly selective.
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Develop and maintain ETL/ELT data pipelines for data ingestion into data warehouse ensuring efficient performance and scalability.
Collaborate with cross-functional data teams to support data infrastructure and maintain data quality and security.
Drive data engineering initiatives end-to-end in an Agile/Product based environment, partnering closely with enterprise data and analytics platforms.
3-5 years of hands-on data engineering experience, including 5+ years in data engineering or software development generally.
Expertise with Databricks, AWS data services (Glue, Redshift, Athena), CloudFormation, GitHub workflows, boto3 APIs.
Strong programming skills in Python, PySpark, Scala or similar; experience with SQL and cloud platforms (AWS preferred).
Work Location: Not explicitly mentioned; Onsite requirements depend on role type (site-essential/site-by-design/remote); notice period not explicitly mentioned.
Experienced in designing and operating data solutions across full lifecycle including data lakehouses, master data management, data quality, and AI/ML integration.
Comfortable working independently in fast-paced Agile/Product-centric teams with global collaboration.
Preferably has functional experience or interest in Life Sciences R&D domain and strengths in improving data processes and architecture scalability.