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Medium due to a common senior data title and metro location, offset by niche Databricks/Spark skills and strong employer.
Medium because core data engineering skills transfer across industries, though regulated life-sciences experience is preferred.
High because JD mandates senior experience plus specific Databricks/Spark/AWS production and governance expertise.
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Lead design, development, and delivery of scalable batch and real-time ETL/ELT data pipelines using Databricks and AWS.
Own complex data platform solutions including metadata-driven integration frameworks, governance, and production support.
Provide technical leadership: define standards, perform architecture and code reviews, mentor engineers, and collaborate across teams for analytics, AI, and DevOps support.
Master's degree with 7+ years experience or Bachelor's degree with 9+ years experience in Computer Science, Engineering, IT, Data Science, or related fields.
Advanced hands-on experience with Databricks, Apache Spark, PySpark, Spark SQL, Delta Lake, Python, and SQL.
Strong expertise in AWS data, compute, storage, security, monitoring, and Databricks Workflows or similar orchestration tools.
Experience implementing data governance, metadata management, lineage, RBAC, encryption, and compliance controls in production-grade data pipelines.
Demonstrated ability to lead technical design and production deployments in enterprise-scale data lakehouse architectures on AWS and Databricks.
Experience operating within regulated industries such as biotechnology, pharmaceutical, manufacturing, or life sciences.
Proven track record defining reusable engineering standards, optimization for performance and cost, and mentoring engineers in Agile/Scaled Agile environments.