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Popular Data Engineer title, metro location and broad skills make applicant competition medium.
Strong regulated-industry governance and platform requirements cause high background sensitivity.
Explicit 8+ years plus mandatory Databricks, PySpark, AWS, and compliance knowledge enforces high strictness.
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Architect and evolve enterprise-grade data platforms enabling advanced analytics, AI/ML, and data-driven decisions.
Lead and mentor data engineering teams, driving platform strategy, governance, and best practices.
Collaborate cross-functionally to ensure secure, compliant, and high-performing data solutions aligned with business goals.
Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
8+ years of experience in data engineering and architecture with leadership on large-scale initiatives.
Expertise in Python and PySpark, with strong hands-on experience with Databricks (Spark, Delta Lake, Workflows).
Experience with AWS services (S3, IAM, Textract, Bedrock or equivalent) and designing scalable document ingestion pipelines.
Experienced in scalable data architectures and governance in regulated industries like healthcare or life sciences.
Skilled in integrating AI orchestration frameworks (MLflow, Airflow) and operationalizing ML models.
Strategic thinker with cross-functional collaboration skills and familiarity with emerging tech like generative AI and vector embeddings.