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Mid-level data engineer title in metro and 3–6 year band increases competition.
Platform-specific dbt and Databricks experience moderately limits cross-industry portability.
Requires 5+ years plus mandatory dbt, Databricks, SQL, Python, and CI/CD expertise.
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Build and deploy scalable, governed, and optimized data transformation pipelines using dbt on Databricks platform for a global data initiative.
Design and maintain high-performance dimensional data models and implement data governance including security and quality checks.
Develop data products supporting generative AI use cases and collaborate globally to implement a standardized cloud-agnostic data platform.
5+ years of hands-on data engineering experience with production-grade solution delivery.
Expert-level proficiency with dbt Core including macros, testing, and CI/CD integration.
Strong practical experience with Databricks Lakehouse Platform, including SQL, Unity Catalog, and data governance features.
Proficient in Python for data ingestion and automation; strong SQL and data warehousing skills; experience with diverse data ingestion sources.
Execution-focused engineer able to deliver high-quality, governed, reusable data assets at global scale in a hybrid collaboration environment.
Experienced with modern cloud data platforms and automation tooling, especially Databricks and GitHub Actions for CI/CD.
Familiar with generative AI concepts and capable of preparing enterprise data to support AI applications and advanced analytics.