Client: Telus Communication, Canada

Designed and implemented advanced AI and data processing solutions, focusing on data governance, curation, and transformation. Led end-to-end design and development of data pipelines, ensuring seamless integration with AI models to enhance customer experience and operational efficiency.

Technologies: GCP, DataFusion, DataProc, BigQuery, GCP Data Lake, Google Gemini, Hugging Face, GPT models, LLMs, AutoML

• Data Governance and Cleansing: Established robust governance policies for customer datasets, ensuring compliance and quality through automated validation workflows.
• Data Curation and Transformation: Designed ETL pipelines for cleaning and restructuring data to improve model accuracy and analytics.
• Data Lake Development: Built scalable, cloud-based GCP Data Lake for storing vast datasets, enabling real-time insights and AI model integration.
• AI Integration: Deployed LLMs and GPT models within Google Gemini for predictive analytics and customer support optimization.

Client: US Bank, USA

Spearheaded the design and solutioning of data ingestion, governance, and curation processes to consolidate fraud insights and enable predictive risk management.

Technologies: Spark, Databricks, Kafka, Python, Tableau

• Designed and developed scalable data pipelines for ingesting high-velocity financial transaction data.
• Implemented robust governance frameworks to ensure data quality and regulatory compliance.
• Optimized ETL processes for real-time anomaly detection and fraud analytics.

Client: Uplight, USA (Google End Client)

Designed and developed data solutions, emphasizing data governance and transformation for energy analytics. Led the integration of curation workflows with analytics platforms to provide real-time energy usage insights.

Technologies: GCP, DataFusion, DataProc, BigQuery

• Built end-to-end pipelines for real-time data ingestion, transformation, and energy data analytics.
• Designed scalable cloud-based data storage and governance solutions to support advanced analytics applications.
• Enhanced data reliability and quality by implementing automated curation and validation processes.

Client: BNY Mellon, USA

Designed and developed scalable data pipelines with a focus on data governance, curation, and transformation for a rule-based billing management system.

Technologies: Spark, Airflow, AWS, Hive, Erwin, Collibra

• Established a robust Data Lake architecture to centralize financial data processing.
• Implemented data transformation processes to meet complex billing requirements.
• Automated workflows to ensure data quality, reduce manual interventions, and optimize billing accuracy.

Client: Nike, USA

Architected data governance and curation frameworks for building an advanced data lake solution. Led the design and development of end-to-end data solutions to support analytics and reporting initiatives.

Technologies: AWS, Spark, Airflow, Hive, Snowflake, Toad Data Modeler

• Designed and implemented AWS-based Data Lake for processing large-scale customer and sales data.
• Developed automated data pipelines with Apache Airflow to improve data availability and reliability.
• Built data transformation workflows to enable Snowflake-powered advanced analytics and reporting.

EISL (UBS, USA): Kafka-based data orchestration and Spark-based data aggregation.

Chevron EDAP: Built enterprise analytics platforms for data filtration and business dashboards.

COOP NDX Loyalty: Migrated and transformed loyalty data using Spark SQL.

AT&T RAPTOR: Deployed Hadoop/Spark/MongoDB-based service assurance systems.

Vodafone Socio Data Archival: Architected CRM archival and analytics systems using Hadoop.