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Metro location, popular Data Engineer title, strong employer brand, and broad Databricks/AWS requirements increase candidate competition.
Core data engineering skills transfer across industries, though pharma/manufacturing domain knowledge moderately increases fit sensitivity.
Explicit 9–12 years plus mandatory Databricks/PySpark/AWS and pipeline experience enforces stringent shortlisting filters.
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Design, develop, and maintain advanced ETL/ELT data pipelines using Databricks, PySpark, Scala, and SQL for large-scale manufacturing data.
Build and optimize data integration frameworks and metadata-driven architectures to enable unified data access, governance, and self-service analytics in hybrid cloud environments.
Collaborate in Agile/Scaled Agile environments with cross-functional teams to deliver data engineering solutions supporting manufacturing and operations use cases.
9 to 12 years of experience in Computer Science, IT, or related fields.
Hands-on expertise with Databricks, PySpark, SparkSQL, Apache Spark, AWS, Python, SQL, and Scaled Agile methodologies.
Strong knowledge of AWS services and workflow orchestration for big data processing.
Any degree; AWS Certified Data Engineer, Databricks Certificate, Scaled Agile SAFe certification preferred but not mandatory.
Senior-level data engineer with deep experience in big data pipeline design and performance tuning in manufacturing or biotech environments.
Familiar with Agile and Scaled Agile (SAFe) delivery frameworks and DevOps practices for continuous improvement.
Operates effectively within cross-functional teams, translating business requirements into scalable technical solutions efficiently.