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High competition due to popular Data Engineer role, metro location, and broad GCP/PySpark skill requirements.
Medium — data engineering skills are transferable, though GCP/PySpark specialization adds moderate domain bias.
Medium because many mandatory technical skills (GCP, PySpark, BigQuery) filter candidates despite no years listed.
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Design, develop, and maintain scalable ETL/ELT data pipelines using PySpark and Python on Google Cloud Platform.
Build and manage data ingestion, transformation, and aggregation solutions using various GCP services including BigQuery, Dataproc, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer.
Optimize PySpark jobs and collaborate with cross-functional teams to ensure data quality, governance, security, and troubleshoot production issues.
Strong experience in Python programming and PySpark for large-scale data processing.
Hands-on experience with Google Cloud Platform services: BigQuery, Dataproc, Dataflow, Pub/Sub, Cloud Storage, Cloud Composer (Airflow).
Strong SQL skills and experience with relational databases.
Work Experience Required: Not explicitly mentioned in the JD.
Experienced in building both batch and real-time data pipelines on cloud platforms, specifically GCP.
Capable of optimizing distributed Spark jobs for performance, scalability, and cost efficiency.
Familiarity with CI/CD pipelines, Agile methodologies, and collaborative development practices involving Data Scientists, Analysts, and Application teams.