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Metro location, common data-engineer title, and reputable global employer increase applicant competition.
Core data engineering skills transfer across industries, though biotech/R&D domain knowledge gives moderate advantage.
Explicit years plus mandatory Databricks/PySpark/AWS and data-architecture skills enforce strict filtering.
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Design, develop, and maintain scalable ETL/ELT pipelines and data integration frameworks supporting structured, semi-structured, and unstructured data.
Optimize big data processing frameworks (e.g., Apache Spark, Hadoop) ensuring high availability, cost efficiency, and query performance for enterprise-scale data.
Implement and manage metadata-driven architectures, data governance, security (RBAC), and develop CI/CD pipelines for automated deployment and monitoring.
3 to 8+ years of experience in data engineering or related field as per education level (Master’s degree with 3-4+ years or Bachelor’s with 5-8+ years).
Hands-on experience with Databricks, PySpark, SparkSQL, Apache Spark, AWS, Python, and SQL.
Experience with enterprise-wide data architectures such as Data Fabric or Data Mesh, and workflow orchestration, performance tuning on big data.
Knowledge or certification in Scaled Agile Framework (SAFe) and Agile/DevOps practices preferred; AWS Certified Data Engineer and Databricks Certification preferred.
Experienced in designing and optimizing big data pipelines in Biotech or Pharma industries with R&D knowledge preferred.
Comfortable working in a cross-functional environment collaborating with data architects, analysts, and DevOps to align data engineering with enterprise goals.
Skilled in continuous improvement of data architectures, metadata management, data governance, and advanced data virtualization techniques.