Priyanka Mangrulkar

Part-time campus role during graduate studies.

• Built an ETL pipeline using Python to extract customer-order data from MongoDB to S3, to support customer analysis •Leveraged AWS Glue (PySpark) to convert raw JSON into structured Parquet, reducing data processing time by 50% •Assisted in automating ETL workflows using Airflow DAGs for daily scheduling, dependency checks, and error handling •Developed dimensional data models (Star Schema) in Redshift, optimizing Tableau dashboards for business insights •Created Power BI dashboards to visualize cook onboarding, user retention, and platform usage KPIs •Wrote complex SQL queries to support ad-hoc analysis and identify churn patterns in user behavior •Cleaned and standardized multi-source data using Python (Pandas), improving consistency in analytics pipelines •Conducted A/B test evaluations on new product features and marketing strategies using statistical testing methods •Delivered weekly data reports to product and marketing teams, accelerating data-driven decision-making •Collaborated with cross-functional teams to define metrics, track performance, and align data solutions with business goals

• Optimized mail tracking and service workflows by analyzing delivery and request data through Excel pivot tables, SQL queries, improving package traceability and reducing processing time by 25%. • Maintained and validated transactional datasets across Salesforce, ensuring data integrity and supporting over 1,000 daily service requests with real-time reporting accuracy. • Developed automated reporting templates in Excel and Google Sheets to visualize operational KPIs such as package turnaround time, staff productivity, and customer service response trends. • Developed interactive Tableau and Power BI dashboards visualizing daily package volume, staff productivity, and service request resolution time that enabled management to identify peak traffic patterns and allocate resources more efficiently • Delivered ad hoc data insights and weekly summaries to management on volume trends, request anomalies, and service metrics, which strengthened data-driven decision-making in campus logistics operations.

Improved avionics system accuracy by 20% by analyzing telemetry & flight performance using Python and MATLAB • Developed predictive models for navigation and control systems to forecast system behavior reducing testing iterations by 15% • Analyzed avionic configuration data and reduced power consumption by 10% using eCalc and developed an interactive dashboard using Tableau to visualize metrics and configurations • Compiled detailed research reports summarizing findings, methodologies, and recommendations, contributing to strategic decisions and ensuring compliance with project requirements

Coursework: signal processing, embedded systems, PCB design, operating systems, C/C++/Python programming. Capstone: patent-awarded pulse diagnostics system using optical sensors and signal classification (Patent No. 202321031953).