Data Engineer
Hi, I'm Prathana.
I'm a data engineer over two years experience in building and supporting enterprise data platforms. I graduated Magna Cum Laude from Boston University with a B.A. in Computer Science, and most recently worked at Fidelity Investments modernizing cloud-based data pipelines across Oracle, Snowflake, and AWS.
When I'm not at my desk, I enjoy crochet, reading, trying new recipes and hiking.
See my work
Experience
Where I've worked
2022 - 2024
Data Engineer · Fidelity Investments
Built and supported integrated enterprise data platforms leveraging Oracle and Snowflake. Migrated legacy batch jobs using AWS and Control-M, automated PII masking pipelines in Snowflake, and boosted CDC pipeline reliability by automating 20+ daily validation jobs across data platforms.
2021 - 2022
Python Teaching Assistant · Boston University
Mentored students on debugging techniques and core Python concepts during biweekly office hours. Instructed three discussion sections with 30+ students and facilitated weekly grading of 300+ assignments.
Skills
Tools I work with
Programming Languages
Databases
AWS Services
Orchestration & Replication
DevOps & Collaboration
Projects
Selected work
Real-Time Urban Air Quality Monitoring Pipeline
Developed a containerized ETL pipeline to ingest air quality data from OpenAQ into a PostgreSQL database. Orchestrated hourly data ingestion with Airflow and visualized air quality trends and measurement latency across global cities.
Analysis of Massachusetts Broadband Access for CBS Boston
Extracted and analyzed 2019 U.S. census data to identify communities around Massachusetts with low rates of internet access. Analyzed the socioeconomic makeup of these areas and constructed geospatial visualizations in R to easily visualize areas with internet access disparities.
Respiratory Illnesses Classification in Lung X-Rays
Developed a convolutional neural network to classify lung X-rays into four categories (COVID-19, viral/bacterial pneumonia and normal). Optimized model performance by comparing VGG-16 vs. VGG-19 architectures over 100 test and train epochs.