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Metro location, popular data title, and broad GCP/PySpark requirements increase applicant density.
Data engineering skills are broadly transferable, though GCP specialization moderately narrows cross-industry fit.
Mandatory GCP, PySpark, and pipeline skills but no explicit years requirement, creating moderate filtering.
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Develop and maintain batch and real-time data pipelines using GCP services like BigQuery, Dataproc, Dataflow, and Pub/Sub.
Implement large-scale data processing solutions primarily using Python and PySpark.
Optimize data workflows integrating Data Lake and Data Warehouse architectures in cloud environments.
Strong proficiency in Python and PySpark programming.
Hands-on experience with Google Cloud Platform services including BigQuery, Dataproc, Dataflow, Cloud Storage, Pub/Sub, and Cloud Composer (Airflow).
Strong SQL skills and experience with relational databases.
Work Experience Required: Not explicitly mentioned in the JD.
Operates effectively in Agile and CI/CD environments with source control management (Git).
Experienced in optimizing distributed computing and Spark performance for large-scale data applications.
Demonstrates in-depth understanding of data engineering concepts, including Data Lake and Data Warehouse design and implementation.