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Metro location, mid-level experience, popular Big Data title and known employer increase candidate competition.
Specialized Big Data and GCP toolset moderately limits cross-industry portability.
Explicit 5.5+ years and many mandatory GCP, BigQuery, PySpark, Kubernetes skills create strict technical filters.
Design, implement, and maintain scalable, reliable Big Data and cloud solutions using GCP (BigQuery, Cloud Functions, Cloud Run), PySpark, Python, and Airflow.
Develop and optimize data workflows, high-volume data processing queries, and scalable APIs using Python frameworks like FastAPI.
Manage containerized applications with Docker and Kubernetes; implement CI/CD pipelines and Infrastructure as Code (Terraform) for cloud resource provisioning and deployment automation.
Minimum 5.5 years of total work experience.
Strong expertise in Google BigQuery, Python, SQL, PySpark, GCP fundamentals, and Kubernetes.
Bachelor’s or master’s degree in Computer Science, Information Technology, or related field.
Hands-on experience with Apache Airflow, serverless GCP services, Docker, Kubernetes, CI/CD tools (GitLab CI/CD, Octopus Deploy), and Terraform.
Senior engineer with deep experience in Big Data engineering and cloud-native GCP environments, focusing on scalable data pipelines and serverless applications.
Experienced in cross-functional collaboration across Data Engineering, Cloud, DevOps, Security, and Application teams to deliver end-to-end solutions.
Proficient in both development and operational aspects including troubleshooting, monitoring, security configurations, and infrastructure automation on GCP.