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Metro location and broad data platform demand but senior, specialized Databricks skills limit applicant density.
Requires deep data engineering and Databricks experience, moderately limiting cross-industry portability.
Explicit 10+ years requirement and leadership plus specific Databricks/Kafka skills enforce strict filtering.
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Lead technical direction and architecture of data foundation for digital twin and AI-powered enterprise platforms.
Oversee design and delivery of robust, scalable, high-performance real-time data pipelines and Lakehouse architecture.
Provide leadership and mentorship to data engineering team, ensuring high-quality, governed data products and DataOps best practices adoption.
10+ years of hands-on data engineering experience with 4+ years in technical leadership.
Experience architecting and delivering scalable production-grade data platforms.
Proficiency with Databricks, Apache Kafka, Delta Live Tables, Unity Catalog, and DataOps practices (CI/CD, automated testing, monitoring).
Work Experience Required: 10+ years data engineering, 4+ years leadership (explicitly mentioned).
Experienced in building data foundations for digital twin or simulation platforms and operational ML workloads.
Demonstrated ability to lead cross-functional, agile teams in complex, fast-paced environments.
Familiarity with industrial domains like Manufacturing, Logistics, or Transportation and real-time operational data use cases.