I'm a passionate data professional with over 6 years of experience in analytics engineering and business strategy.
I’m a passionate data professional with over 6 years of experience in analytics engineering and business strategy. I thrive at the intersection of data and decision-making, helping businesses leverage metrics for growth and efficiency. I enjoy collaborating with cross-functional teams to create scalable data models and insights that drive success.
Outside of work, I love exploring new analytical tools and techniques. I believe that continuous learning is key to staying ahead in the ever-evolving data landscape. I’m also an advocate for data literacy, striving to empower others with the knowledge and skills needed to harness the power of data effectively.
This project focuses on the programmatic retrieval and analysis of high-frequency financial time-series data. Utilizing Python and the Binance API, I engineered a robust script to harvest historical pricing data for major cryptocurrencies (BTC, ETH) with 1-minute granularity.
This project builds an intelligent underwriting system that revolutionizes small business financing by going beyond traditional credit scoring. Inspired by the sophisticated risk assessment models used by Square Capital and Ramp, this engine leverages alternative data sources and advanced machine learning to provide real-time credit risk assessments.
Key Innovation: Unlike traditional underwriting that relies heavily on financial statements and credit scores, this system incorporates digital footprint data, customer sentiment, and transaction patterns to create a comprehensive risk profile.
Owned the end-to-end analytics engineering layer, partnering with marketing, sales, and leadership to define KPI logic and enable self-serve analytics in Looker. Refactored models into a Medallion architecture using SQL, reducing debugging time and improving scalability for downstream analytics tables by 30%. Led integration of Shopify data via Fivetran and GA4 data via BigQuery and Snowpipe into Snowflake, processing 1M+ records and cutting ELT runtime from 10 to 3 minutes. Developed a customer segmentation model in Python, translating insights into campaign strategy that improved marketing efficiency by 10% and increased user conversion by 20%.
Led analytics delivery across 4 enterprise client projects, aligning data models and dashboards with executive and regulatory reporting requirements. Designed and automated financial and actuarial reporting pipelines, reducing manual reporting effort by 40% and supporting multi-year client engagements. Built ETL pipelines for actuarial models using Python and SAS on an on-premise platform, processing 5M+ records monthly; optimized staging and partitioning to cut runtime by 30% across insurance clients. Created automated data integration template using SQL, SAS DI Studio, and SAS Management Console to ingest data from actuarial systems, accelerating audit processes by 40%.
Led analytics initiatives for a leading financial institution, analyzing 10M+ records across 5 years to monitor loan repayment, early withdrawal, and liquidity risk. Designed stress-testing and crisis simulation models to support regulatory and executive decision-making. Re-architected loan prepayment models by migrating logic from R to SQL, resolving memory constraints and enabling faster scenario iteration. Automated data pipelines between Oracle and analytical workflows, reducing runtime from 3 days to 1 hour.
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