Shaifali  ·  Lead GCP Data Engineer  ·  10+ yrs

Overview: This project involved designing and implementing a petabyte-scale data warehouse on GCP. Responsibilities: Designed and implemented a petabyte-scale data warehouse on GCP. Optimized BigQuery queries, reducing execution time by 40%. Automated ETL workflows using Apache Airflow for seamless data processing. Established best practices for cost optimization and data security. Ensured compliance with data protection regulations via encryption and PII masking. Led infrastructure automation using Terraform for efficient resource management.

Overview: This project focused on migrating on-premise data sources to GCP BigQuery and optimizing ETL processes. Responsibilities: Migrated on-premise data sources (CSV, SQL, Oracle, MongoDB) to GCP BigQuery. Developed optimized ETL pipelines with Dataflow and Apache Beam, improving efficiency by 30%. Designed access control mechanisms for secure data governance. Conducted performance tuning, reducing data processing costs by 25%. Integrated Looker for enhanced data visualization and reporting. Ensured minimal downtime during the migration process.

Overview: Built an end-to-end data pipeline for predictive analytics on financial transactions. Responsibilities: Built an end-to-end data pipeline for predictive analytics on financial transactions. Designed efficient data models in BigQuery, reducing retrieval latency by 50%. Implemented machine learning workflows for fraud detection using Python. Automated ETL processes for real-time financial data processing. Integrated Looker for business intelligence and financial reporting. Ensured compliance with financial data security standards.

Overview: Designed and implemented a real-time data ingestion pipeline and event-driven architecture. Responsibilities: Designed and implemented a real-time data ingestion pipeline using Pub/Sub and Dataflow. Developed event-driven architecture for streaming data processing. Optimized BigQuery storage for efficient data querying. Integrated monitoring solutions using Stackdriver and Prometheus. Automated pipeline deployment using Terraform and Apache Airflow. Ensured fault tolerance and high availability for critical data flows.

Overview: Designed a centralized data lake on GCP for retail analytics. Responsibilities: Designed a centralized data lake on GCP for retail analytics. Developed scalable ETL jobs using Dataflow and Apache Beam. Led cost-optimization efforts, reducing data storage expenses by 20%. Integrated customer behavior analytics into business intelligence dashboards. Improved query performance and data retrieval speeds. Ensured data security through encryption and access control mechanisms.