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models using dbt.\nStrong understanding of BigQuery partitioning, clustering, cost optimization, and query-performance tuning.\nExperience with GKE, Kubernetes namespaces, Docker, and container-based application deployment.\nStrong experience with Cloud Monitoring, Cloud Logging, alerting, observability, and production monitoring.\nExperience with Git-based development, branching strategies, pull-request reviews, CI/CD pipelines, Terraform, and Harness or similar deployment tools.\nAbility to lead technical discussions, perform design and code reviews, and provide technical guidance to team members.\nExperience in effort estimation, solution documentation, stakeholder communication, and production incident management.\nStrong troubleshooting skills with ownership of root-cause analysis, defect resolution, and preventive actions.\nAbility to coordinate with business, architecture, infrastructure, and application teams for end-to-end delivery\nKey Responsibilities\nStrong experience in Data Engineering and designing scalable data solutions on Google Cloud Platform.\nAbility to design end-to-end architecture for batch and real-time data-processing solutions.\nAdvanced hands-on experience with BigQuery, Dataflow, Cloud Composer, Pub/Sub, Dataproc, GCS, and Cloud Run.\nStrong expertise in Python, SQL, Apache Beam, PySpark, and performance optimization.\nExperience in designing and implementing complex ETL/ELT pipelines and data models using dbt.\nStrong understanding of BigQuery partitioning, clustering, cost optimization, and query-performance tuning.\nExperience with GKE, Kubernetes namespaces, Docker, and container-based application deployment.\nStrong experience with Cloud Monitoring, Cloud Logging, alerting, observability, and production monitoring.\nExperience with Git-based development, branching strategies, pull-request reviews, CI/CD pipelines, Terraform, and Harness or similar deployment tools.\nAbility to lead technical discussions, perform design and code 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