Likhith Bhargav

Built a Python Dash app and automated Excel-based valuation workflows to save 12+ hours/week; applied Monte Carlo simulations to stress-test assumptions and improved financial projection reliability. Streamlined structured data ingestion into AWS S3, reducing spreadsheet dependency by 50% and enabling audit-ready traceability.

Designed and deployed ELT pipelines with Apache Airflow to ingest REST API data into Snowflake, reducing pipeline errors by 40% and improving data freshness. Delivered self-service Power BI dashboards that cut ad-hoc data requests by 60% and implemented Python/dbt monitoring to achieve 99.5% SLA adherence.

Engineered predictive models on 2M+ economic records, identifying 3 high-impact predictors and improving model precision by 22%. Built Power BI dashboards on BigQuery, reduced report turnaround by 45%, and automated ETL with Airflow/dbt-style SQL to improve runtimes by 60%.

Engineered scalable ETL pipelines using AWS Glue/S3/EC2 and advanced SQL to reduce processing time by 35% and enable real-time portfolio analytics on 5M+ weekly records. Integrated Kafka with Glue for near-real-time events, lowering pipeline latency by 40% and feeding 10+ executive Tableau dashboards.

Designed data ingestion workflows with Python, SQL, and Azure Synapse to accelerate reporting cycles by 40% and improve data availability across 5+ business units. Optimized stored procedures and views to reduce report generation time by 30% and deployed Flink for near-real-time log processing, lowering latency by 35%.

Grade: 3.53

Master of Science in Business Analytics (STEM) completed with a GPA of 3.53; focused on data engineering, analytics, and applied machine learning for business decisioning.

Grade: 3.70

Bachelor of Engineering in Information Science completed with a GPA of 3.70; coursework and projects covered software engineering, data systems, and analytics.