Shivani Polsani

Engineered scalable data pipelines for trade and portfolio data, enabling seamless integration across multiple financial systems and improving reliability of downstream analytics. Improved data accuracy by 35% by implementing automated validation frameworks and anomaly detection models using Python and SQL. Designed and optimized analytics-ready datasets using Snowflake, dbt, and data modeling techniques, accelerating risk analysis, compliance reporting, and portfolio insights. Reduced data latency and enhanced pipeline reliability by orchestrating batch and real-time workflows using Apache Airflow, Kafka, and Spark Structured Streaming. Built and contributed to a centralized lakehouse architecture (ADLS, Databricks, Delta Lake) to support scalable, AI-driven financial analytics. Processed large-scale financial datasets using PySpark and Spark, applying data cleansing and transformation techniques to ensure high-quality data delivery.

Developed scalable ETL pipelines to ingest and process large-scale healthcare data, improving accessibility and reliability for analytics and reporting. Increased data accuracy by 40% by implementing robust data quality checks and standardizing healthcare datasets aligned with national health data standards. Improved data processing efficiency by 35% through optimized ETL workflows using PySpark, Airflow, and SQL Designed and implemented data models for healthcare datasets, enabling efficient storage and retrieval of patient, diagnostics, and hospital records. Processed high-volume healthcare datasets using Hadoop and PySpark, ensuring compliance with clinical data standards (SNOMED CT). Monitored and maintained real-time and batch data pipelines using Kafka, Airflow, AWS S3, and Redshift, ensuring system reliability and performance.

Analyzed large-scale retail and e-commerce datasets using SQL, Python, and BigQuery to uncover customer behavior and sales trends. Improved demand forecasting accuracy by 30% and increased conversion rates by 20% through predictive modeling and advanced analytics. Designed star schema-based data models to support scalable analytics and reporting using BigQuery and Hadoop. Built customer analytics and forecasting models using Python (Scikit-learn) to drive inventory planning and marketing strategies. Reduced stock-outs and improved customer retention through cohort and funnel analysis using SQL, Python, and BI tools (Power BI/Tableau).

• Focused on advanced topics in cloud computing, distributed systems, and scalable software architecture. • Completed coursework in cloud infrastructure, machine learning, data engineering, and software system design, strengthening expertise in building modern cloud-native applications. • Developed hands-on projects involving containerized applications, cloud platforms, and data-driven systems, applying DevOps and infrastructure automation principles. • Collaborated on research-driven and practical projects emphasizing scalability, performance optimization, and real-world problem solving.