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Mid-level generalist data engineer with popular tech stack and 3+ years, attracting many qualified applicants.
Core data engineering skills are highly transferable across industries despite life-sciences domain preference.
Multiple mandatory technical skills (PySpark, AWS, Redshift, Airflow) plus explicit 3+ years requirement.
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Build and maintain scalable data pipelines and infrastructure to support enterprise-wide data and analytics needs.
Implement data governance, quality checks, and secure data infrastructure in collaboration with cross-functional teams.
Collaborate with data scientists and stakeholders to operationalize models and enable self-service analytics using tools like Tableau, Looker, and Power BI.
Bachelor’s degree in Engineering, Analytics, Data Science, Computer Science, Statistics, or equivalent experience.
Minimum 3+ years of experience with big data technologies including Python, PySpark, SQL, AWS data services (Redshift, S3, Glue, Lambda, EventBridge, Postgres).
Experience building batch, micro-batch, and streaming pipelines and familiarity with data modeling techniques and data governance frameworks.
Work Experience Required: 3+ years in relevant big data engineering roles; Notice Period: Not explicitly mentioned in the JD.
Experienced in designing and delivering enterprise-scale data platforms including data lakehouse, warehouse, and marts with metadata-driven and medallion architectures.
Familiarity with modern data orchestration and deployment practices such as Airflow, Agile (SAFe), and CI/CD pipeline implementations.
Comfortable translating complex technical requirements into scalable solutions and working closely with data scientists and business stakeholders in a regulated environment like life sciences or pharmaceuticals.