Software Development Computer Engineering

DAIVIK GANGAPPA

********@***.*** 680-***-**** LinkedIn GitHub Syracuse, NY

SUMMARY

Computer Engineering graduate student with 2.8 years of experience in software development, cloud architecture, and DevOps. Proficient in AWS, CI/CD, Python, and SQL, optimizing cloud deployments for up to 80% faster rollouts. Skilled in designing scalable, high-performance solutions with automation and Agile methodologies across industries. EDUCATION

Syracuse University M.S in Computer Engineering August 2023 – May 2025 PES Institute of Technology B.E. Electronics & Communication August 2016 – August 2020 EXPERIENCE

Teaching Assistant, Syracuse University – Syracuse, NY March 2025 – Present

• Facilitating hands-on Python sessions and conducting classroom lectures covering OOP, debugging, unit testing, libraries, and file I/O, ensuring 100% student engagement and successful project completion. Teaching Assistant, Syracuse University – Syracuse, NY June 2024 – July 2024

• Instructed Python programming for the MIT BWSI Autonomous RACECAR course, achieving 100% project completion by guiding students through simulations, robotics applications, and real-world implementations. Member Technical Staff, MetricStream Infotech – Bangalore, India August 2022 – June 2023

• Optimized production deployments for 5+ enterprise clients, improving uptime and reliability with Apache-Tomcat and Oracle Database.

• Implemented CI/CD pipelines, reducing deployment time by 80% and optimizing system performance with MySQL, Unix shell scripting, and Apache-Tomcat, ensuring reliable application delivery and seamless client experience. Application Development Associate, Accenture Solutions – Bangalore, India January 2021 – August 2022

• Architected and executed cloud migration, transitioning Abinitio and DataStage jobs to Azure (ADF, Databricks), automating workflows, and ensuring 100% SLA compliance with Python and SQL.

• Optimized ETL operations using Azure Data Factory, Databricks, and Snowflake, reducing data processing time by 50% and enabling real-time analytics through Agile-driven execution. SKILLS & TECHNICAL EXPERTISE

• Cloud & DevOps: AWS (Architecting Scalable, Fault-Tolerant Solutions, IAM, EC2, S3, Lambda, RDS, VPC, Load Balancing, Auto Scaling, CloudFormation), Azure (Data Factory, Databricks, ADLS), CI/CD (Automated Pipelines, Jenkins, Docker, Kubernetes), Unix/Linux Environments, Apache-Tomcat.

• Programming & Software Development: Python (Pandas, NLTK, NumPy, Sklearn, TextBlob), Java (Spring Framework, MVC, OOP), SQL, C++, Unix Shell Scripting, HTML, CSS.

• Data Engineering & Analytics: ETL Automation, Data Wrangling, Data Mining, Data Transformation, SQL, Power BI (DAX, Power Query), Machine Learning Models (Random Forest, Naïve Bayes, Decision Trees, Logistic Regression).

• Database Management: MySQL, Oracle Database, Microsoft SQL Server, Snowflake, Teradata.

• Software & Tools: Git, GitHub, Jira, SDLC, Agile Methodologies. CERTIFICATIONS & AWARDS

• AWS Certified Solutions Architect – Associate Mar 2025 – Mar 2028

• Microsoft Certified: Azure Data Fundamentals

• Employee of the Month – Awarded for performance excellence.

• Spotted Award – Recognized three times for outstanding contributions. PROJECTS

Book Inventory Management System – Java Spring Boot, React

• Engineered a responsive, command-driven book inventory management system leveraging Java Spring Boot (backend) and React (frontend), integrating design patterns (Strategy, Command, Builder, Observer) to facilitate efficient book buyback and sales transactions. Elevated user experience and functionality, supporting real-time book status updates and inventory tracking.

University Medical Center HR Database – SQL Server, Power BI

• Developed a streamlined HR database in SQL Server for the University Medical Center, ensuring data integrity in recruitment workflows. Generated real-time dashboards and reports through SQL and Power BI, enabling data-driven decision-making and elevating operational efficiency. Social Media Sentiment Analysis on U.S. Presidential Influence – Python, NLP

• Analyzed over 500,000 tweets from 2020 election employing machine learning (Random Forest, Naive Bayes) and NLP tools

(VADER, TextBlob, NLTK) to detect shifts in public sentiment on U.S. President. Succeeded with 85% accuracy and refining sentiment analysis by 70%, revealing key sentiment trends pre- and post-election.

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