U.S. Bank, USA | Jan 2024 - Current

• Designed and automated ETL pipelines using SQL, Python, and Azure Data Factory, improving data ingestion efficiency by 45% and ensuring seamless integration across multiple financial systems.
• Developed and maintained interactive Power BI and Tableau dashboards, providing real-time insights into credit portfolio performance, delinquency rates, and fraud metrics, increasing executive reporting accuracy by 30%.
• Conducted in-depth data validation, cleansing, and quality checks using Python (pandas, NumPy) and SQL procedures, reducing reporting errors by 25% and ensuring compliance with data governance standards.
• Collaborated with cross-functional teams to establish data governance frameworks covering data lineage, ownership, and metadata management, improving data transparency and trust across departments.
• Created forecasting and trend analysis models using regression, clustering, and time-series techniques to predict customer churn and transaction behaviors, improving marketing campaign targeting by 20%.
• Optimized data models and schema designs in Snowflake and Azure Synapse, enhancing query performance and reducing data retrieval time by 40% across analytical workloads.
• Built automated KPI reports to monitor loan performance, customer segmentation, and credit utilization trends, enabling leadership to make faster and data-backed strategic decisions.
• Applied A/B testing and statistical analysis to evaluate new credit policies and promotional offers, improving customer acquisition efficiency by 15% while reducing risk exposure.
• Partnered with the data engineering and DevOps teams to implement CI/CD workflows (Git, Jenkins, Azure DevOps) for analytics pipelines, reducing manual deployment time by 50%.
• Leveraged Databricks (PySpark) to process high-volume transactional data and generate near real-time insights for fraud detection and anomaly identification.
• Ensured compliance with HIPAA, PCI DSS, and internal audit requirements by enforcing data validation, encryption, and governance best practices across analytical environments.
• Collaborated with stakeholders across finance, compliance, and operations to translate analytical findings into business recommendations, driving measurable improvements in operational performance and revenue growth.
• Implemented data-driven process automation using Power BI APIs and Python scripts, reducing manual data refresh cycles by 60 % and improving report delivery speed across business units.

Abbott, India | Jan 2020 - Dec 2022

• Developed and automated ETL pipelines using SQL, Python, and AWS Glue, streamlining data ingestion from ERP, CRM, and manufacturing systems, which improved data availability by 40%.
• Designed and deployed Power BI and Tableau dashboards for monitoring sales performance, inventory management, and supply chain KPIs, reducing manual reporting time by 35%.
• Conducted comprehensive data validation, cleansing, and reconciliation across multiple data sources, improving data accuracy by 28% and aligning with corporate data governance policies.
• Built robust data models (Star & Snowflake Schema) in Snowflake and Azure Synapse, optimizing analytical workflows and enhancing query performance by 30%.
• Partnered with cross-functional business units to define data standards, lineage, and metadata documentation, ensuring consistent and compliant data usage across departments.
• Utilized Python (pandas, NumPy, scikit-learn) to perform statistical analysis and forecasting, identifying key demand patterns that improved production planning efficiency by 22%.
• Integrated predictive analytics and machine learning models to forecast product demand and detect anomalies in quality metrics, reducing supply chain disruptions by 15%.
• Enhanced data governance and compliance by implementing data validation rules, access controls, and audit logs, ensuring adherence to HIPAA and ISO data quality standards.
• Implemented process automation using SQL stored procedures and Power Query, cutting repetitive reporting tasks by 25% and improving analyst productivity.
• Collaborated with data engineering and QA teams to maintain version control and ensure continuous improvement of analytical workflows through Git and Azure DevOps.
• Presented actionable insights to senior management using visual storytelling and KPI dashboards, directly influencing pricing and product distribution strategies.
• Authored detailed technical documentation, SOPs, and data dictionaries, improving data literacy and ensuring knowledge transfer across analytics and business teams.