Orchestrated PII data migration from an on-premises MDM shifted system to Google Cloud Platform (GCP) Dataflow with Apache Beam 2.54, auditing 30% boosted the speed of data transformation.Engineered a high-performance, wide-column NoSQL database using BigTable, enabling fast and scalable storage and moving the data into RDBMS achieving 55% augmented the speed.Audited Data Errors with Airflow and Python and identified procedural areas for improvement, resulting in a 19% increase in the profitability of the MDM Project.Modernized a data transformation solution in Data Stage, rectifying errors in source system files ingestion; achieved a 50% enhancement in query optimization by streamlining data transfer processes.Architected the 4 Million Records of sensitive banking data from an on-premises upgraded system to Google Cloud with assistance Databricks, resulting in a 40% reduction in data Sync time.Implemented the state-of-the-art data processing system for a leading banking institution, leveraging DBT and BigQuery; achieved a remarkable boosted business insights speed by 25%.

Designed and implemented data warehousing solutions, enabling efficient storageand retrieval of structured and unstructured data Improving data accessibility andperformance, enabling effective analysis and reporting for [BI] Team.Streamlined data governance frameworks and policies, ensuring data quality,integrity, and compliance with regulations for Reference Data improving dataconsistency, accuracy, and security, ensuring compliance with data privacyregulations and industry standards.Implemented a real-time messaging system using Pub/Sub, enabling efficient andreliable communication between various components of the data ecosystem forDBT Models reducing data latency by 75% and improved the overallresponsiveness of the data infrastructure.Transformed a machine learning model for fraud detection using Vertex AI and MLAlgorithm achieving 90% accuracy in identifying fraudulent transactions, resultingin a significant reduction in financial losses.Strengthened and implemented a NoSQL database solution using Firestore,enabling fast and scalable data storage for the client data, achieving real-time datasynchronisation, and improving data access and performance.Engaged with product managers, analysts, and senior management, I have playeda pivotal role in optimizing data pipelines and revolutionizing productivity KPIs.Through my expertise as a GCP Data Engineer resulting in a remarkable 35%improvement in productivity.

Audited System/Data/Timeout Error Data, identified procedural areas forimprovement, resulting in a 19% increase in the profitability of the OMNI Project.Modernised tools to perform automated verification testing and assist in capturingserial data link requirements, which resulted in an increase in efficiency of 55%.Orchestrated expert Large Language Models (LLM) for OMNI BOT deliveringfactual correct reports on a daily/weekly/monthly basis, achieving 77% accuracy inrevenue forecasting.Developed and maintained data pipelines using Cloud DataFusion and Python,enabling efficient data integration and transformation, achieving seamless datamovement and transformation across various sources, and improving overall data.Led and coached Product Owners to improve story writing, feature discovery, andrequirements gathering process which led to an increase in Organisation satisfaction anda decrease in time-to-delivery by 20%

Revamped a scalable data warehousing solution using BigQuery, enabling efficientdata storage and retrieval for analytics and reporting reducing query responsetime by 40% and improved overall data analysis performance.Automated the deployment of ML models into the production environment,reducing development time by 20%Built an automated system to predict whether a given review was likely to be froma real user, leading to a reduction in "fake" reviews by 7%.Engineered a high-performance, wide-column NoSQL database using BigTable,enabling fast and scalable storage for large-scale data, achieving high throughputand low-latency data access, and supporting real-time applications with massivedata volumes.