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Design, implement, and optimize data solutions on Google Cloud Platform, focusing on BigQuery and large-scale data processing.

Requirements

• Strong expertise in Google BigQuery – performance tuning, partitioning, clustering, and cost optimization.
• Hands-on experience with ETL tools and frameworks, including Cloud Dataflow (Apache Beam), Cloud Dataproc (Spark), and Cloud Composer (Airflow).
• Proficiency in SQL, Python, and Shell scripting for data transformation and automation.
• Experience with Google Cloud Storage, Pub/Sub, and Cloud Functions.
• Strong understanding of GCP IAM, security best practices, and data governance.
• Familiarity with Terraform, Cloud Build, and CI/CD for infrastructure automation.
• Ability to work with large-scale datasets and real-time streaming data processing.

Responsibilities

• Design, develop, and optimize BigQuery data warehouses, ensuring high performance and cost efficiency.
• Build and maintain scalable ETL pipelines using GCP services such as Cloud Dataflow, Cloud Dataproc, and Cloud Composer (Airflow).
• Implement data ingestion, transformation, and processing workflows using Python, SQL, and Spark.
• Work with Cloud Storage, Pub/Sub, and Cloud Functions to facilitate real-time and batch data processing.
• Ensure data security, governance, and compliance using IAM, encryption, and audit logging.
• Troubleshoot performance issues, optimize queries, and manage BigQuery cost and resource utilization.
• Implement CI/CD pipelines for data workflows using Terraform and Cloud Build.

Other

• 10+ years of experience working as a GCP Data Engineer or similar role.
• Collaborate with Data Scientists and Analysts to optimize query performance and enable advanced analytics.
• Strong analytical and problem-solving skills.
• GCP Professional Data Engineer Certification is a plus.
• Experience with Machine Learning workflows on GCP (Vertex AI, AutoML, TensorFlow on AI Platform) is a plus.