Data Analytics (GCP)

5 to 7 Years of experience in Looker Studio,Original,Pro, Looker Cloud..

• Act as go-expert for customer issues, provide RCAworkarounds and best practices to customers and internal teams.

• Own and resolve complex issues for Google Cloud Data Analytics products like BigQuery,Storage, Airflow Composer, Dataflow, Pub/Sub.

• Deep dive into logs, query plans, job graphs and pipeline failures, toidentify root causes.

• Strong expertise in Troubleshoot data pipeline failures, latency issues, quota errors, Monitor system health and job failures.

• Exposure in configuring and managing network devices and services

• Own incidents end-to-end: triage, root cause analysis, resolution, and communication with stakeholders.

• Strong knowledge in IAM, VPC, storage, quotas, and GCP project setup. Experience with Python, SQL, and Shell scripting for troubleshooting. Familiarity with CI/CD, Git, and Infrastructure as Code.

• Strong analytical and problem-solving skills for production issues. Excellent communication skills to explain technical issues to both technical and non-technical stakeholders.

• Use Cloud Logging, Cloud Monitoring to collect logs, metrics and traces of debugging. Reproduce the customer issues in the test environment and validate fixes before roll out.

• Advanced knowledge of BigQuery performance tuning including materialized views, partitioning strategies, and clustering to optimize high-volume queries.

• In-depth experience with Apache Beam programming model for complex Dataflow transformations, including stateful processing and side inputs.

• Ability to design and implement highly available and scalable data architectures in GCP, including cross-region disaster recovery and failover strategies.

• Proficiency in advanced Airflow concepts, such as custom operators, dynamic DAG generation, and implementing robust error handling and alerting frameworks.

• Expert-level debugging of distributed systems, utilizing advanced tracing (Cloud Trace) and profiling techniques to identify bottlenecks in complex data pipelines.

• Diagnosing root causes and resolving technical issues across hardware, software, and networking systems.

• Delivering technical support to internal and external users via multiple communication channels.

• Provisioning and deprovisioning cloud infrastructure, including compute, storage, and networking components, as required.

• Articulating technical concepts clearly and effectively to stakeholders and end users.