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Metro location and established employer increase applicants, but seniority and Databricks/AWS specialization limit competition.
Core data engineering skills transfer across industries, though life-sciences domain experience is advantageous.
Explicit 8+ years, Databricks and AWS SME requirements, and leadership expectations make filtering stringent.
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Lead design, architecture, and delivery of enterprise-level data engineering and analytics solutions leveraging AWS cloud and Databricks.
Own end-to-end development and maintenance of data products, ETL/ELT pipelines, data models, and ensure data quality and security.
Manage multiple stakeholders, provide technical leadership and guidance to data engineering teams, and collaborate with global teams and vendors.
8+ years of hands-on experience in data engineering with cloud (AWS preferred) and Databricks expertise.
Proven experience as a solution architect for enterprise data engineering and analytics solutions.
Strong programming skills (Python, PySpark, Scala), and experience with AWS data services (Glue, Redshift, Athena, etc.).
Work Experience Required: 8+ years practical experience in relevant technical domain. Notice Period: Not explicitly mentioned in the JD.
Experienced leader capable of managing and mentoring data engineering teams in a globally distributed, Agile/product-based environment.
Strong strategic mindset with ability to evaluate, prioritize data initiatives, and provide architectural guidance for scalable data solutions.
Deep technical expertise in AWS cloud architecture, Databricks, and integrating AI/ML and automation into data platforms, preferably with exposure to life sciences domain.