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Tier-1 employer, metro location, popular senior data-engineer title, and broad cloud/Databricks skill requirements.
Core data engineering skills transfer across industries, but biotech R&D and governance needs increase domain specificity.
Explicit 8-13 years requirement and mandatory AWS Data Engineer certification and specific Databricks/Stack skills.
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Lead and mentor a team of data engineers in designing, building, and maintaining large-scale data pipelines and analytics solutions using Databricks, Spark, and cloud platforms (AWS).
Oversee data ingestion, transformation, validation, and governance to ensure high-quality, compliant, and efficient data processing and accessibility.
Drive Agile/SAFe project delivery, operational monitoring, self-healing pipeline implementations, and alignment of data architecture with business objectives.
Master's or Bachelor's degree in Computer Science, IT, or related field.
8 to 13 years of professional experience in data engineering or related disciplines.
Must have experience managing a team of data engineers and hands-on skills with Python, PySpark, SQL, Databricks, and cloud platforms (AWS).
Professional certification: AWS Certified Data Engineer mandatory; familiarity with Databricks Certificate preferred.
Experienced leader capable of managing and coaching data engineering teams in complex R&D or enterprise environments.
Strong technical background in building scalable data ingestion, transformation, and analytics pipelines using big data technologies and cloud infrastructures.
Proficient in Agile/SAFe methodologies and skilled in aligning data solutions with fast-paced business requirements and compliance frameworks.