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Metro mid-level data engineer role with common title but specialized Databricks/Kafka stack yields moderate competition.
Requires specific Databricks, streaming, and governance experience with simulation/digital-twin focus, so strong domain bias.
Explicit 5-7 years requirement plus specific Databricks, Kafka, and governance tooling makes screening strict.
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Lead architecture and delivery of scalable, high-performance data pipelines for digital twin and AI-powered enterprise platforms.
Ensure high-quality, governed data products with strong data lineage, quality, and governance using Unity Catalog and Delta Lake.
Collaborate closely with data architects, simulation engineers, ML engineers, and platform teams to deliver real-time operational intelligence and simulation-ready data.
5 to 7 years of hands-on data engineering experience.
Experience with modern DataOps practices including CI/CD for data pipelines, automated testing, and monitoring.
Proficiency with Databricks, Apache Kafka, Delta Lake, Unity Catalog, and Python/Scala/SQL for data transformation and orchestration.
Work Experience Required: 5 to 7 years.
Demonstrated ability to architect and deliver production-grade data platforms with scalable, low-latency real-time data pipelines.
Experience or strong interest in digital twin or simulation platforms, physics-informed or operational ML feature engineering.
Comfortable working in fast-paced, ambiguous environments and collaborating cross-functionally with engineering and product teams.