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Mid-level popular data role in a metro with broad GCP/Python requirements increases candidate competition.
Requires strong, specific GCP data-engineering expertise, limiting easy cross-industry transferability.
Explicit 6+ years plus mandatory GCP, Python, SQL, and pipeline experience makes filtering strict.
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Design, develop, and maintain scalable and reliable data pipelines on Google Cloud Platform (GCP).
Develop and optimize ETL/ELT data processing and transformation solutions using Python and SQL for large datasets.
Oversee data quality, security, governance, and troubleshoot production pipeline issues while participating in technical design and architecture decisions.
6+ years of experience in Data Engineering.
Strong hands-on experience with Google Cloud Platform (GCP) services such as BigQuery, Cloud Storage (GCS), Cloud Composer/Airflow, Dataflow, Pub/Sub, and Dataproc.
Proficient in Python programming and advanced SQL (including complex queries, joins, CTEs, window functions, and query optimization).
Experience developing ETL/ELT pipelines; knowledge of data warehousing, data modeling, and distributed data processing.
Experienced in building large-scale, production-grade data pipelines on GCP with operational focus on reliability and performance.
Skilled in integrating multiple GCP data services and applying data governance and security best practices.
Pragmatic in CI/CD, version control (Git), and familiar with Agile development environments; additional experience with Apache Spark/PySpark and Infrastructure as Code (Terraform) is preferred but not mandatory.