Sahanya Pogaku
@sahanyapogaku
I’m a mid-level data engineer building scalable GCP pipelines and BI insights for business teams.
United StatesWhat I'm looking for
I’m a Data Engineer with 2+ years of experience building scalable data solutions on Google Cloud Platform. I turn large datasets into actionable insights by combining strong SQL and Python analysis with BI tools like Power BI, Tableau, and Looker.
At University of South Florida, I designed and maintained 15+ Power BI dashboards to monitor exam accommodations, testing volumes, SLA compliance, and student service metrics—improving reporting visibility by 40%. I analyzed 5,000+ student test records using Excel, SQL, and Python to improve accessibility services by 15%, and I optimized SQL queries and reporting views to improve data retrieval efficiency by 25%.
At JP Morgan Chase, I designed real-time and batch data pipelines with GCP Dataflow, Pub/Sub, and BigQuery, improving data availability by 30%. I reduced pipeline failures by 25%, lowered query execution costs by 28%, improved dashboard response time by 35%, and implemented multi-zone high availability with 99.9% pipeline uptime. I’m also comfortable with ML work, having built a profanity detection model with 95% accuracy and a churn model that contributed to a 9% month-over-month retention improvement.
Experience
Work history, roles, and key accomplishments
Designed and maintained 15+ Power BI dashboards and reports for exam accommodations, testing volumes, SLA compliance, and student service metrics. Analyzed 5,000+ student test records using Excel, SQL, and Python and optimized SQL queries/stored procedures to centralize data and improve reporting efficiency.
Designed and implemented real-time and batch data pipelines on GCP for customer transaction data supporting pre-approved loan eligibility analysis. Built ETL workflows with GCP Composer (Apache Airflow), developed PySpark transformations for analytics-ready models, and optimized BigQuery performance and costs for reporting.
Developed and deployed a Python profanity detection model to filter and mask inappropriate content in internal communication datasets. Built a churn prediction model in Python using historical engagement data to support retention strategies.
Education
Degrees, certifications, and relevant coursework
University of South Florida
Master of Science, Computer Science
2024 - 2026
Earned a Master of Science in Computer Science at the University of South Florida (Aug 2024–May 2026), including projects such as reasoning-augmented LLM work and a geospatial crop recommendation system.
Availability
Location
United StatesAuthorized to work in
Job categories
Skills
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