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Popular mid-level data role with a known global employer and broad GCP/PySpark requirements.
Data engineering skills transfer across industries, but GCP/Airflow specificity creates moderate industry lock-in.
Explicit 6+ years plus mandatory GCP, PySpark, BigQuery, Terraform, and DataOps skills increase screening strictness.
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Design, build, and maintain scalable data pipelines and ETL/ELT solutions on Google Cloud Platform using Python, PySpark, and SQL.
Lead DataOps activities such as deployment automation, CI/CD, monitoring, incident resolution, and operational support across the data platform.
Support infrastructure provisioning with Terraform and manage containerized data platform deployments using Google Kubernetes Engine (GKE).
Minimum 6 years of hands-on experience in Data Engineering.
Proficiency in Python, PySpark, advanced SQL, and production-grade ETL/ELT pipeline development.
Experience with Google Cloud Platform services including BigQuery, Cloud Storage, Dataflow, Cloud Composer (Apache Airflow), and Secret Manager.
Hands-on knowledge of CI/CD pipelines, Terraform (Infrastructure as Code), and container orchestration (GKE).
Experienced in managing end-to-end cloud-native data engineering projects with operational ownership from development to production support.
Skilled in implementing DataOps/DevOps practices to ensure platform reliability and automation.
Capable of collaborating across multi-disciplinary teams (Data Engineers, Analytics, Product Owners) in Agile environments to deliver enterprise-scale cloud data solutions.