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Common data-engineer title, metro location, and mid-level seniority increase applicant competition.
Core data engineering skills are broadly transferable, though GCP specialization slightly narrows applicability.
Multiple mandatory GCP, PySpark, and data pipeline skills increase filter strictness.
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Develop and maintain batch and real-time data pipelines on Google Cloud Platform.
Optimize data processing workloads using PySpark and distributed computing techniques.
Manage and deploy data engineering solutions leveraging GCP services like BigQuery, Dataproc, Dataflow, Pub/Sub, and Cloud Composer.
Strong Python programming skills.
Hands-on experience with PySpark for large-scale data processing.
Experience using Google Cloud Platform services including BigQuery, Dataproc, Dataflow, Cloud Storage, Pub/Sub, and Cloud Composer.
Strong SQL skills and experience with relational databases.
Proficient in building and optimizing scalable data pipelines in both batch and streaming contexts.
Experienced in applying Spark optimization and distributed computing principles for performance.
Familiarity with CI/CD pipelines and Agile methodologies indicating ability to work in structured delivery environments.