Saniasnain M.

Telecommunication Data Migration Project

• Designed a tokenization solution and utilized a tokenization framework to efficiently process data at scale across 400+ pipelines.
• Led and supervised the Data Acceleration Program, successfully migrating all Revenue Assurance systems from on-premises to Google Cloud.
• Coordinated with multiple vendors to ensure on-time project deliverables.
• Acted as Test Lead for all modules, ensuring high-quality testing standards were maintained.
• Managed cloud environments and executed cloud computing strategies to align with business goals.
• Developed ETL pipelines using a versatile framework with plug-and-play capabilities for multiple data sources to GCP targets like Google BigQuery, Google Cloud Spanner, and Cloud SQL.
• Leveraged underlying Google services, including PubSub, Google Dataflow, Google Dataproc, and Cloud Composer, for data ingestion, loading, and framework orchestration.
• Continuously improved the framework by adding new capabilities to optimize underlying Google services' usage.
• Addressed in-life data issues on Hadoop and Oracle Exadata.Key Accomplishments:
• Entrusted to lead the overall program’s testing efforts within a short period of time.
• Increased performance of existing pipelines at least by 55% using Google’s best practices.
• Actively worked on a framework to improve and reduce the SLAs for incremental systems significantly.

Healthcare Data Migration Project

• Worked on Client Facing Data Engineering role to architect propose and finalize the solution.
• Worked on creating and finalizing the Technical Design Document through numerous revisions and presentations.
• Actively led a team effort of ingesting more than 7.8 Petabytes of historical DICOM files from on-premise to Google Cloud Storage.
• Created a native Python based orchestration application to load/monitor/validate historical loads from Google Cloud Storage to Google Healthcare store(s).
• Developed a Google Dataflow/Apache Beam application to ingest incremental HL7 messages from more than 5 source systems via PubSub and to update the medical record in FHIR stores and also the DICOM images in DICOM stores of Google Healthcare APIs.
• Designed a robust auditing and monitoring system using Google BigQuery and Cloud monitoring to generate alerts and maintain audits for both incremental and historical loads.
• Performed historical as well as incremental loads between the on-premise Oracle database and Google Cloud Spanner to keep them in sync.
• Worked in an Agile Development environment with continuous delivery in sprints.
• Maintained Code versions using Azure DevOps Git Repositories and automated deployments using Azure DevOps CI/CD Pipelines.Key Accomplishments:
• The data enabled Data scientist to start testing their models with an intention to detect terminal diseases using enhanced ML capabilities of Google Cloud and also ensured that both medical professionals as well as the patients have a centralized access to all their medical history.
• Leveraging autoscaling on Cloud Dataflow I achieved a maximum throughput of 2 million records/second.
• Received an ‘Applause Award’ for excellent client demos and deliverables.