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Tier-1 employer, metro location, popular Data Engineer title, and broad skill requirements drive high competition.
Core data engineering skills are transferable, but life-sciences domain experience is preferred, so moderate sensitivity.
Explicit 7+ years plus mandatory Databricks, Python, SQL, AWS and observability experience makes filtering strict.
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Design, implement, and maintain complex data engineering solutions and pipelines connecting multiple data stores and applications.
Develop and operate observability capabilities (metrics, logs, alerts) ensuring data workload reliability and support L3 production issues as SME.
Collaborate globally to optimize data infrastructure, automate onboarding of new data sources, and deliver standard reports per stakeholder needs.
Bachelor's degree in Information Technology, Computer Science or related Technology field.
7+ years of experience in developing data pipelines and infrastructure, ideally in drug development or life sciences.
Strong skills in Python, SQL, AWS; practical experience with Databricks and data ingestion/curation tools.
Experience with data engineering practices, software versioning, release management; Agile methodology familiarity.
Experienced in handling large-scale information management projects with data integration and self-service analytics.
Strong background in cloud-native environments, preferably AWS certified, with knowledge of distributed computing and storage.
Proven ability to develop data observability tooling, automate operations, troubleshoot production incidents, and work within matrix organizational structures.