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Tier-1 employer, metro location, and common data-engineer skillset increase applicant density.
Core data engineering skills transfer across industries but biotech/manufacturing preference raises domain specificity.
Explicit 9–12 years plus mandatory Databricks/PySpark/AWS skills create strict filters.
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Design, develop, and optimize complex ETL/ELT data pipelines in Databricks using PySpark, Scala, and SQL for large-scale manufacturing datasets.
Develop data integration frameworks and metadata-driven architectures enabling unified data access, governance, and interoperability across hybrid cloud environments.
Collaborate in Agile/SAFe teams to deliver data engineering solutions supporting manufacturing and operations analytics, including AI-driven insights.
9 to 12 years of Computer Science, IT or related field experience.
Hands-on experience with Databricks, PySpark, Apache Spark, SparkSQL, AWS, Python, SQL, and Scaled Agile methodologies.
Strong knowledge of AWS services and big data workflow orchestration and performance tuning.
Degree in any field (no specific degree mandated).
Experienced in data engineering within biotech, pharma, or manufacturing environments with familiarity of relevant data sources (SCADA, Data Historians).
Proficient in developing automated, reusable data pipelines and working in SAFe Agile teams with DevOps practices.
Comfortable with large-scale data processing, data modeling for OLAP/OLTP databases, and integrating diverse data types from APIs, logs, and event streams.