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Metro location and generalist Data Engineer title increase competition, specialized Databricks/Kafka skills moderately reduce density.
Data engineering skills are broadly transferable, but digital twin and operational domain experience increases sensitivity.
Explicit 7+ years and mandatory Databricks, Kafka, Delta Live Tables requirements make shortlisting highly strict.
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Design, develop, and maintain scalable data pipelines using Databricks, PySpark, Delta Lake, and Kafka to support digital twin platforms and real-time operational analytics.
Implement data quality checks with Delta Live Tables and manage data governance through Unity Catalog to ensure compliance and data integrity.
Optimize pipeline performance and collaborate with ML engineers to deliver feature-engineered datasets while supporting production data systems via monitoring and incident resolution.
7+ years of hands-on data engineering experience.
Proficiency with Databricks, PySpark, Delta Lake, Kafka, and Delta Live Tables.
Experience building and maintaining production-grade data pipelines in real-time and batch processing environments.
Work Experience Required: 7+ years of data engineering; Notice Period: Not explicitly mentioned in the JD.
Candidates with experience in operational data modeling, time-series data patterns, and building data pipelines for digital twin or simulation platforms.
Strong familiarity with data governance, quality enforcement, and scalable pipeline optimization in agile, cross-functional teams.
Experience in industrial domains such as Manufacturing, Logistics, or Transportation is highly desirable but not mandatory.