Hello!

I'm Parth Gawande



Data Analyst | Data Engineer

RIT Graduate

Python • SQL • AWS • BI Tools

LinkedIn My works

Name: Parth Gawande

Experience: 2 Years

Address: San Francisco, California, USA

Open to Full-Time Roles

Skills

SQL 95%
PYTHON 92%
Data Visualization 90%
Statistical Analysis 92%
Machine Learning 85%
Data Engineering and ETL 90%
Generative AI 82%

About

About Me

I am a data professional with a Master’s in Information Technology and Analytics from Rochester Institute of Technology (GPA: 3.9), combining strong academic expertise with industry experience to design, build, and optimize end-to-end data solutions. My work bridges data analytics and data engineering, allowing me to manage the full data lifecycle from ingestion and transformation to visualization and actionable insights.

Over the past 2+ years, I have delivered measurable business impact across transportation, telecom, logistics, retail, and public sector projects, including:

  • Cutting annual operational costs by $600K through route optimization analytics.
  • Accelerating SLA-bound reporting timelines by 60% using automated streaming pipelines.
  • Reducing monthly cloud costs by $40K with query and storage optimization.
  • Improving anomaly resolution speed by 22–66% via proactive monitoring systems.

My expertise spans Python, SQL, and R for data manipulation and modeling; Snowflake, Redshift, BigQuery, and Databricks for scalable warehousing; and Airflow, AWS Glue, Terraform, and CI/CD for automation and infrastructure. On the analytics side, I create KPI-rich, interactive dashboards using Tableau, Power BI, QuickSight, and Looker Studio, enabling leaders to make confident, data-driven decisions.

I excel at aligning technical execution with strategic goals, ensuring data is accurate, insightful, and impactful, empowering organizations to act faster and smarter.

0 +   Projects completed

Github

Resume

Resume

Results-driven Data Analyst & Engineer with 2+ years of experience delivering measurable business impact by designing scalable data solutions, applying advanced analytics and machine learning, and translating complex datasets into actionable strategies that optimize performance and drive growth.

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Skills

Skills & Expertise

Structured skillset grouped by domain and proficiency, combining tools, platforms, and techniques across analytics, engineering, and cloud technologies.

Programming & Analytics

Python

SQL

R

Java

C++

PySpark

Business Intelligence & Visualization

Tableau

Power BI

Looker Studio

QuickSight

D3.js

Reporting & Data Tools

Excel

Google Sheets

dbt

Data Engineering & Automation

Apache Spark

Apache Kafka

Kubernetes

Docker

PostgreSQL

MongoDB

Cloud & Platforms

AWS

Google Cloud

Azure

Snowflake

Databricks

AWS Technologies

Amazon EC2

Amazon S3

AWS Lambda

Amazon RDS

Amazon Redshift

AWS Glue

Machine Learning & Data Science

Jupyter

TensorFlow

PyTorch

Scikit-learn

Keras

Kaggle

Web Technologies

HTML5

CSS3

JavaScript

React

Node.js

Version Control & Deployment

Git

CI/CD

Experience


Data Engineer

September 2025 - Present Saayam Logo Saayam For All

Saayam For All is a mission-driven nonprofit organization dedicated to empowering underserved communities by providing access to essential resources, education, and healthcare services. It leverages data-driven strategies and innovative technology to optimize program outcomes and maximize social impact across multiple regions.

  • Analyzed over 15,000 donor records from Salesforce and Excel to identify data inconsistencies and trends using SQL and Excel, contributing to the development of intuitive Power BI dashboards that enhanced stakeholder visibility and improved donor engagement insights by 30%.
  • Spearheaded KPI development and collaborated with fundraising and program teams to deliver detailed data analysis and reporting; automated SQL- and Python-driven workflows improved report accuracy and delivery speed by 25%, driving more efficient resource allocation decisions.
  • Executed comprehensive data cleansing, transformation, and integration across multi-source datasets; improved data quality and consistency by 20%, enabling more reliable business intelligence and operational insights that supported donor engagement strategies.
  • Designed and deployed 7+ interactive dashboards with Power BI, showcasing donor behavior metrics and fundraising performance, increasing cross-team transparency by 35% and fostering data-driven strategy adjustments for five active programs within tight deadlines.
  • Partnered with program managers and finance teams to define and track 15 KPIs, applying descriptive analytics methods that uncovered actionable operational insights and boosted fundraising program efficiency by 18% over six months.

Graduate Teaching Assistant

January 2024 - May 2025 RIT Logo Rochester Institute of Technology

Rochester Institute of Technology (RIT) is a top-ranked U.S. university known for its strong focus on technology, innovation, and experiential learning. It offers leading programs in data science, computing, and analytics with a strong industry connection.

  • Delivered 1,000+ instructional hours to 200 undergraduates in database systems and SQL, creating hands-on labs on query optimization, joins, indexing, and normalization using real-world datasets. This approach improved student proficiency by 20% on average, as measured by exam scores, and prepared 85% of students for industry-ready SQL project work.
  • Partnered with six faculty members to redesign 15 SQL assignments, introducing progressive complexity and integrating industry datasets from logistics and healthcare domains. These curriculum updates improved retention by 25%, increased lab participation rates by 30%, and reduced dropout rates in advanced modules over three consecutive semesters.
  • Developed automated grading scripts and standardized rubrics to evaluate SQL code efficiency, accuracy, and adherence to relational modeling standards. This reduced grading turnaround time by 40%, ensured consistent evaluation across all sections, and enabled faster delivery of feedback to more than 200 students each semester.
  • Incorporated authentic datasets from operational industries such as logistics and healthcare into teaching materials, enabling students to practice normalization, indexing strategies, and multi-table joins on realistic data. This resulted in a 35% increase in practical application skills, as measured through improved capstone project quality and instructor evaluations.
  • Conducted over 60 one-on-one mentoring sessions for underperforming students, diagnosing conceptual gaps and designing targeted exercises. This personalized approach improved individual exam scores by an average of 15% and reduced overall course failure rates by 20%, ensuring more students successfully met learning objectives.

Data Analyst

July 2021 - January 2023 StandardWings Logo StandardWings Technologies Pvt. Ltd.

StandardWings Technologies Pvt. Ltd., headquartered in Nashik, India, is a leading IT solutions provider specializing in web and mobile development, IoT, AI/ML systems, and SAP implementation. With over 10 years of experience, 500+ successful projects, and 400+ satisfied clients across government, logistics, healthcare, and fintech sectors, StandardWings delivers innovative, scalable, and user-centric solutions through agile methodologies, transparent communication, and a strong commitment to quality.

  • Extracted, cleaned, and integrated multi-source fleet data from 120+ vehicles using SQL and Python into AWS S3, enabling detailed operational analysis that identified cost savings and improved overall fleet efficiency by 15% through data-driven insights
  • Collaborated with cross-functional teams to define 21 key KPIs aligned with business goals; developed automated monitoring reports that enhanced visibility into fleet performance, contributing to continuous improvement and achieving a 15% gain in operational efficiency
  • Designed and implemented 14 interactive Tableau and Power BI dashboards visualizing route efficiency, fuel consumption, and SLA compliance; enhanced real-time operational transparency, empowering logistics, operations, and finance teams to improve resource allocation.
  • Established data governance and validation processes for over 600,000 records using SQL and Python, reducing inconsistency by 25%, while implementing AWS Lambda-triggered real-time alert workflows that cut fleet delay responses by 22%, improving operational responsiveness.
  • Partnered with management and technical teams to deliver actionable insights through comprehensive reports, driving strategic initiatives that optimized fleet utilization and reduced operational delays, resulting in measurable cost reductions and enhanced service levels across logistics operations.

Data Analyst Intern

January 2021 - June 2021 StandardWings Logo StandardWings Technologies Pvt. Ltd.

StandardWings Technologies Pvt. Ltd., headquartered in Nashik, India, is a leading IT solutions provider specializing in web and mobile development, IoT, AI/ML systems, and SAP implementation. With over 10 years of experience, 500+ successful projects, and 400+ satisfied clients across government, logistics, healthcare, and fintech sectors, StandardWings delivers innovative, scalable, and user-centric solutions through agile methodologies, transparent communication, and a strong commitment to quality.

  • Partnered with hospital management and data teams to extract, clean, and analyze over 25,000 patient records using SAP and Excel; developed data visualizations that contributed to a 20% reduction in patient wait times during COVID-19 response efforts.
  • Created and maintained Power BI and Tableau dashboards tracking hospital capacity, staffing, and equipment availability across 12 facilities, enabling data-driven resource allocation that reduced shortages and optimized patient care by 25%
  • Conducted comprehensive data quality checks and validation on clinical datasets, improving accuracy by 15%; collaborated with IT teams to streamline data workflows that enhanced reporting efficiency and reduced manual data reconciliation efforts.
  • Assisted in building automated reporting processes integrating SAP and Excel data extracts using SQL and Python, accelerating report generation time by 30% while ensuring timely and reliable distribution to stakeholders.
  • Supported cross-functional teams in monitoring key healthcare KPIs and generating actionable insights from patient data, driving improvements in operational planning and contributing to a coordinated pandemic response at the regional level.



Education


MS in Information Technology and Analytics

January 2023 - May 2025 RIT Logo Rochester Institute of Technology

Grade: 3.9/4.0

Overview: Interdisciplinary program blending computing foundations with analytical rigor, equipping me to solve complex business problems through data-driven insights. Emphasis on advanced data technologies, statistical modeling, visualization, and decision science.

Key Coursework: Data Science & Analytics Foundations, Database Design & Implementation, Non-Relational Data Management (MongoDB, NoSQL), Visual Analytics (Tableau, Power BI), Information Retrieval & Text Mining, Time Series Analysis & Forecasting, Data Warehousing, Data-Driven Knowledge Discovery (ML-based analytics), Capstone in Information Technology & Analytics.

Highlights: Honed proficiency in Python, R, SQL, and modern data ecosystems; actively participated in tech seminars, analytics meetups, and interdisciplinary research events hosted by the Golisano College of Computing and Information Sciences, gaining exposure to emerging technologies and collaborative innovation.

B.Tech in Computer Science and Engineering

July 2018 - July 2022 MIT Logo MIT World Peace University

Grade: 9.27/10.00

Overview: Strong technical foundation in computing and analytics, combining engineering principles with practical problem-solving, software development, and algorithmic thinking. Program emphasized hands-on, implementation-first learning.

Key Coursework: Data Structures & Algorithms, Object-Oriented Programming (OOP), Operating Systems, Database Management Systems, Computer Networks, Machine Learning, Artificial Intelligence, Software Engineering & Agile Methodologies, Web Development & Cloud Infrastructure, Cybersecurity, Blockchain Fundamentals, Applied Mathematics.

Highlights: Consistently achieved first-class standing; engaged in coding contests, technical paper presentations, peer mentoring, and student development clubs, refining teamwork, public speaking, and leadership abilities while fostering a collaborative engineering mindset.

Projects

Projects

Below are the sample Data Analytics projects on SQL, Python, Power BI & ML.

HealthLens: Monitoring Healthcare Performance and Trends

HealthLens is a data-driven analytics project that uncovers operational inefficiencies in healthcare using Python-based EDA and interactive Power BI dashboards. By integrating multiple tables like billing, prescriptions, and diagnoses, it enables hospital administrators to track KPIs, spot anomalies, and make evidence-based decisions. Tools used include Pandas, Seaborn, Plotly, Power BI, and SQL.

CareAllocate: Healthcare Resource Allocation

CareAllocate models optimal distribution of limited healthcare resources (beds, staff, ventilators) across regions using linear programming and constraint optimization. Built with Python, Pandas, PuLP, and Streamlit, it helps policymakers or hospital admins make critical real-time decisions during pandemics or emergencies. Visual outputs support transparency and faster strategic planning.

ForecastPro: Scalable Sales Forecasting via MLOps

ForecastPro is a robust MLOps pipeline that automates sales forecasting using Prophet and Scikit-learn. It includes CI/CD, model registry, and experiment tracking via MLflow and DVC. Designed for scalability, the system forecasts product demand and supports better inventory planning. The stack includes Python, FastAPI, Docker, GitHub Actions, and Streamlit.

AcuMedica: Medical Performance Dashboard

AcuMedica is a Power BI–driven dashboard that visualizes key healthcare performance metrics such as patient outcomes, diagnosis frequency, and treatment trends. Using DAX, Power Query, and structured healthcare data, it provides stakeholders with actionable insights to improve clinical decision-making, resource planning, and operational efficiency in hospitals or clinics.

InfoSnip: Tailored News Summarization

InfoSnip is a tailored news summarization tool designed to tackle the growing problem of information overload by transforming extensive news articles into concise and easily digestible summaries. Leveraging advanced Natural Language Processing (NLP) models, InfoSnip efficiently condenses long news pieces, making it easier for users to stay informed without spending excessive time reading full-length articles.

Dyslexia Detection Using Machine Learning

This project contains a machine learning-based solution for detecting dyslexia using behavioural and cognitive data. Dyslexia Detection leverages advanced machine learning models to identify dyslexic patterns in individuals, aiding in early diagnosis and intervention. The project demonstrates how data-driven approaches can enhance diagnostic processes, potentially providing more accurate and faster results than traditional methods.

GeoEnvision: Satellite Image Enhancement and Topography Detection for Urban Planning

Deep learning has revolutionized the analysis and interpretation of satellite and aerial imagery, addressing unique challenges such as vast image sizes and a wide array of object classes. This repository provides an exhaustive overview of deep learning techniques specifically tailored for satellite and aerial image processing. It covers a range of architectures, models, and algorithms suited for key tasks like classification, segmentation, and object detection.

Black Gold Horizon: Projecting America's Oil Future

This repository is dedicated to the project "Black Gold Horizon: Projecting America's Oil Future", a comprehensive analysis and forecast of crude oil production in the United States, both at the national level and for specific regions. The project utilizes advanced time-series analysis and forecasting models in R to provide predictions of future production levels. The goal of the project is to aid policymakers, industry professionals, and researchers in making data-driven decisions in the energy sector.

YouTrendify: Youtube Video Insights Engine

YouTrendify is a machine learning-based tool designed to provide predictive insights into YouTube video metrics, with a focus on subscriber growth, video ranking, and revenue estimation. By leveraging regression models, feature engineering, and advanced hyperparameter tuning, YouTrendify allows YouTube content creators and analysts to make data-driven decisions that can improve content strategy and performance.

Analyzing GASTech Building Operations Data

This project, completed as part of the VAST Challenge 2016, focuses on analyzing operational data from the GASTech building. The data includes employee movements, HVAC sensor readings, and environmental parameters such as CO2 and Hazium gas levels. The goal of the project is to identify patterns, detect anomalies, and understand causal relationships between employee behavior and building conditions..

StyleSync: AI-Driven Personalized Outfit and Shopping Assistant

StyleSync is an AI-powered fashion compatibility system that analyzes user-uploaded clothing images and recommends visually and contextually compatible outfits. Built on the Maryland Polyvore dataset using a ResNet-50 and Multi-Layered Comparison Network (MCN) architecture, the system scores outfit compatibility and suggests matching items. It incorporates SerpAPI for real-time product recommendations with clickable links and includes a user feedback loop to personalize suggestions based on thumbs up/down ratings. The solution balances deep learning, API integration, and intuitive UI design to deliver a smart, personalized styling experience.

Data Analysis on Airbnb in New York City

This project involves the creation of a visually informative dashboard to analyze Airbnb listings in New York City. The goal is to provide insights into rental prices, availability, and other key metrics across the boroughs of NYC using data visualization techniques. The dashboard enables users to interact with the data, making it easier to derive meaningful patterns and trends in the rental market.

F1Track – Real-Time Performance Analytics Using Databricks

Built a Formula 1 Data Engineering project using Spark on Azure Databricks and Delta Lake architecture. Formula 1 season happens once a year roughly 20 races. Each race happens over a weekend. Roughly 10 teams (constructors) participate in a season. Each team have two drivers who participate in the race. Two drivers get qualified from the entire team and they get to start the race earlier. Each driver can have multiple pit stops to change tires or fix damaged car. Based on the race results, driver standings and constructor standings are decided. The top of the drivers standings becomes the drivers' champion and the team that tops the constructor standings, becomes the constructors' champion.

Churnlytics: Telecom Customer Retention Risk Modeling

This project focuses on analyzing customer churn within a telecommunications company using the Telco Customer Churn Dataset. The primary goal is to develop predictive models to assess customer churn and monthly charges. Several machine learning techniques, such as regression, classification, and clustering, were employed to extract insights and predict customer behavior.

Publications

Research Work

Authored technical publications on data analytics and machine learning, focusing on forecasting models, performance analysis, and real-world applications..

IEEE CONIT: Video-Based Human Activity Detection

Co-authored and published research at IEEE CONIT 2022 on human activity recognition using deep learning and computer vision. Built a 3D CNN with incremental learning to classify human actions (walking, jogging, running, boxing, waving, clapping) on the KTH dataset. Achieved 98.88% accuracy, outperforming prior methods while preventing catastrophic forgetting and enabling scalable real-time analysis.

A Survey Paper on Malware Detection Methods

This paper delves into the complexities of modern malware, which has advanced from simple, single-purpose software to sophisticated polymorphic variants, posing significant challenges to cybersecurity efforts. Traditional malware detection methods, reliant on signature-based classification, are increasingly ineffective against these evolving threats. Our research provides a comprehensive overview of contemporary malware detection strategies, emphasizing cutting-edge approaches such as artificial intelligence (AI), machine learning (ML) classification, deep learning, autoencoders, and IoT cloud environments.

Combination of Transfer Learning and Incremental Learning for Anomalous Activity Detection in Videos

Proposed a system combining transfer learning and incremental learning for human action recognition in videos. The model addresses challenges like catastrophic forgetting and retraining costs by learning new actions without losing prior knowledge. Evaluated on KTH and UCF101 datasets, it shows improved accuracy and efficiency over existing approaches.

Climate Change: Preliminary Analysis and Prediction Using Global Temperature Data

Published research in American Journal of Electronics & Communication (2022) on predictive climate analytics. Analyzed 60+ years of global temperature and emissions data (NASA, UN) using regression models to forecast long-term temperature trends. Delivered insights on correlations between CO2 emissions, deforestation, and temperature anomalies through advanced data wrangling and statistical modeling.

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0 Projects
0 Mentored Students
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More projects on Github

I love to solve business problems & uncover hidden data stories


GitHub

Contact

Contact Me

Below are the details to reach out to me!

Address

San Francisco, CA

Contact Number

+1 585 625 9131

Email Address

pg5598@rit.edu

Download Resume

Resume



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