Designed scalable ETL/ELT pipelines using Azure Data Factory and Azure SQL Database across 10M+ banking records, improving processing performance by 30%

Built optimized SQL data models, stored procedures, and views powering real-time Power BI dashboards for compliance and operations stakeholders

Implemented SCD Type 1 & 2 dimensional models for AML/BSA regulatory reporting with automated incremental load management

Engineered feature pipelines for anomaly detection and fraud monitoring using SQL, Python, and PySpark, reducing false positives by 15%

Led A/B testing data frameworks and experiment-tracking pipelines enabling statistically validated decision-making

Maintained end-to-end data lineage documentation across 25+ audit-ready deliverables with 99.5% completeness via Unity Catalog

Built cloud data pipelines using Azure Data Factory, Data Lake Gen2, Synapse Analytics, and Databricks processing 20M+ records monthly

Developed batch and near real-time pipelines with PySpark, Auto Loader, Delta Lake, and Delta Live Tables for ML and analytics workloads

Implemented event-driven orchestration with ADF triggers and Databricks Workflows, reducing cycle time from 2 days to 4 hours

Built medallion architecture data layers (Bronze/Silver/Gold) supporting analytics and regulatory reporting

Designed end-to-end ETL pipelines on AWS using S3, AWS Glue, and Amazon Redshift for enterprise reporting

Built automated financial reconciliation workflows achieving 98% match accuracy and reducing manual effort by 50%

Developed Power BI dashboards connected to Redshift supporting 6+ operational workflow

• Designed and developed ETL pipelines and data architectures to streamline data ingestion and reporting processes.

• Leveraged SQL and BI tools to create dashboards, performance metrics, and reports for client requirements.

• Engineered data acquisition processes, handling both batch data at rest and streaming data to enable real-time data availability and system integrity.

• Identified and implemented process improvements to enhance the reliability and scalability of ETL workflows.