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Common data engineering stack, mid-senior title, and metro/office setting produce moderate candidate competition.
Data engineering skills on PySpark and GCP are broadly transferable across industries.
Specific mandatory techs (Python, PySpark, GCP) and senior expectations increase filter rigidity.
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Design, develop, and maintain scalable data pipelines using Python and PySpark.
Work on GCP-based data platforms integrating multiple structured and semi-structured data sources.
Optimize and support Spark jobs and data workflows for performance, scalability, cost efficiency, and reliability.
Proficiency in Python and PySpark for data pipeline development.
Experience with GCP data platforms and managing both batch and near-real-time workflows.
Knowledge of enterprise data governance, security, and compliance standards.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building scalable data solutions on cloud platforms, especially GCP.
Skilled in performance optimization of Spark jobs and complex data processing workflows.
Able to collaborate with data architects, platform teams, and stakeholders for end-to-end data solution delivery.