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Popular Data Engineer title, metro location and broad stack increase applicant density.
Core data engineering skills transfer across industries, though digital-twin/time-series focus adds domain specificity.
Explicit 7+ years requirement plus mandatory Databricks, Kafka, and Delta expertise tightens filters.
Design, develop, and maintain scalable real-time and batch data pipelines powering digital twin platforms and operational analytics using Databricks, PySpark, Kafka, and Delta Lake.
Implement data governance, quality checks, and metadata management via Unity Catalog and Delta Live Tables while optimizing pipeline performance and cost.
Collaborate closely with data architects, ML engineers, and simulation teams to deliver high-quality, governed datasets and support production systems including business data warehouse solutions with Terradata.
7+ years of hands-on data engineering experience, including building and maintaining production-grade data pipelines.
Strong expertise with Databricks, PySpark, Delta Lake, Kafka, and Delta Live Tables.
Experience implementing data governance and quality validation using Unity Catalog and Delta Live Tables expectations.
Work Experience Required: 7+ years
Experienced in building data pipelines for real-time operational systems, ideally with digital twin or simulation platform exposure.
Knowledgeable in operational state modeling and time-series data or physics-informed ML feature engineering.
Comfortable working in agile, distributed, multidisciplinary teams, preferably with exposure to industrial domains like Manufacturing, Logistics, or Transportation.