Srishti Garg

Software Engineer

Panipat, Haryana, India11 mos experience

Key Highlights

3-star coder at Codechef
National award winner at Code with Cisco'24 hackathon
Crafted impactful MERN projects

Stackforce AI infers this person is a Full Stack Developer with a strong focus on Data Processing and Machine Learning in SaaS.

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Skills

Core Skills

Apache SparkScalaData ProcessingPythonMachine Learning

Other Skills

Python (Programming Language)Large Language Models (LLM)hudiAmazon Web Services (AWS)Amazon Elastic MapReduce (EMR)Competitive ProgrammingDatabase Management System (DBMS)Operating SystemsObject-Oriented Programming (OOP)MySQLMongoDBExpress.jsReact.jsCascading Style Sheets (CSS)HTML

About

I am a passionare Computer Science graduate from JIIT, Noida, maintaining a robust academic record with a commendable 9.0 CGPA. My expertise lies in C++ programming, where I've honed my skills to become a 3-star coder at Codechef and Pupil at Codeforces. I've tackled over 500 Data Structures and Algorithms problems on Leetcode, GeeksforGeeks and CodingNinjas showcasing my commitment to mastering the art of problem-solving. I have worked as an SWE Intern at Google in the ML /AI domain which helped a lot in the growth of my skills. My passion extends to Full Stack development, where I've crafted impactful MERN projects. I have been a national award winner at Code with Cisco'24 hackathon. Having a technical inclination while also being inherently creative and a people's person, I wish to encash my knowledge and skills and apply them in "techno-creative" domains.

Experience

11 mos

Total Experience

11 mos

Average Tenure

11 mos

Current Experience

Google

Software Engineer

Jul 2025 – Present · 11 mos · Hyderabad, Telangana, India · On-site

Amazon

SDE Intern

Jan 2025 – Jun 2025 · 5 mos · Bangalore · On-site

Worked in the Transportation Financial Systems (TFS) organization, contributing to systems responsible for calculating end-to-end shipment cost at an individual item level. Leveraged Spark and Scala to build scalable data processing solutions and worked on a high-impact initiative aimed at improving cost visibility and operational insights.
Key Contributions & Impact:
Designed and built a “Single Source of Truth” data platform to track the complete lifecycle of an item across multiple transportation and allocation stages.
Enabled Hudi read capability in the Vuitton service, forming the foundational layer for downstream analytics and dashboarding.
Architected the condensed data model and schema to unify heterogeneous datasets into a standardized, analytics-friendly structure stored in a compressed S3 data path.
Developed custom Transformers to extract, normalize, and consolidate data from 12+ category buckets (filtered, error, retry, allocated, unallocated, etc.), requiring deep analysis of varied data contracts and ingestion behaviors.
Enhanced the configuration-driven ingestion framework by:
Adding support for multi-S3 path ingestion
Extending transformation configuration schemas to onboard new transformers seamlessly
Implemented daily scheduled jobs to automate data extraction, transformation, and condensation pipelines.
Optimized storage layout by partitioning compressed datasets across key parameters, significantly improving lookup efficiency and query performance.
Conducted POCs with cost analysis to evaluate architectural approaches and select the most cost-efficient design.
Delivered a user-friendly operational dashboard that simplified investigation workflows and improved on-call efficiency.
Result: Reduced business query turnaround time from minutes to seconds, enabling faster decision-making, improved observability, and scalable cost analytics across transportation workflows.