Prathmesh Patil

Software Engineer

Maharashtra, India2 yrs 8 mos experience

Key Highlights

Engineered scalable services handling 5M+ daily requests.
Developed ETL pipelines improving processing time by 40%.
Automated processes reducing on-call queries by 30%.

Stackforce AI infers this person is a Backend Engineer specializing in SaaS solutions with a focus on microservices and data processing.

Contact

Skills

Core Skills

AwsMicroservices

Other Skills

APMAWS GlueAWS S3AlgorithmsAmazon EC2Amazon ECSAmazon S3Amazon SQSAmazon Web Services (AWS)Apache SparkAzure DevOps ServicesC (Programming Language)C++Cascading Style Sheets (CSS)Continuous Integration and Continuous Delivery (CI/CD)

About

With over a two years of experience as a software engineer, I specialize in backend development and thrive on solving complex, real-time challenges through code. I am a self-motivated and solutions-oriented professional who excels in collaborative environments and takes pride in effectively managing responsibilities. I focus on engineering innovative solutions, optimizing system performance, and working with cross-functional teams to deliver high-quality software products. Driven by a passion for learning and problem-solving, I continuously seek opportunities to grow and contribute meaningfully to impactful projects. Let’s connect to explore technology, development, and collaboration opportunities!

Experience

2 yrs 8 mos

Total Experience

1 yr

Average Tenure

8 mos

Current Experience

Colortokens inc.

Member of Technical Staff - I

Oct 2025 – Present · 8 mos · Bengaluru, Karnataka, India · Hybrid

Anakin (yc s21)

Software Developer

Jun 2024 – Oct 2025 · 1 yr 4 mos · Bengaluru, Karnataka, India · On-site

Engineered and deployed highly scalable, event-driven scraping services on AWS, capable of handling 5M+ daily requests with 99.9% uptime, using Redis for efficient caching and Dockerized microservices for seamless deployment.
Designed and built a distributed, event-driven compiler service to automate and standardize data
transformation workflows. Efficiently processed 2+ TB of data monthly from MongoDB and AWS S3, delivering outputs in JSON, Parquet, BigQuery, and REST APIs
Developed robust, scalable ETL pipelines using AWS Glue and Apache Spark, processing 100M+ records per day across multiple data formats (JSON, Parquet), leading to a 40% improvement in processing time and increased pipeline reliability, reducing delivery latency by 30%.
Optimized large-scale MongoDB operations by implementing advanced indexing strategies, query profiling, and tuning — achieving 50% faster execution times and supporting 50M+ documents with minimal performance degradation.

AWSRedisDockerMongoDBAWS S3ETL+2

Smallcase

Backend Engineer

Aug 2023 – Apr 2024 · 8 mos · Bengaluru, Karnataka, India · Remote

Enhanced observability and traceability by implementing cross-application tracing across microservices, enabling seamless monitoring and debugging of distributed systems.
Designed and implemented compliance-driven features to meet regulatory changes, significantly reducing the business workload and ensuring adherence to legal requirements.
Addressed memory utilization challenges caused by database overloading by optimizing query projections to load only essential data. This optimization reduced memory and operational load by 25%, leading to improved concurrency and system performance.
Automated the manager offboarding process on an internal tool by leveraging REST APIs, reducing engineering dependency, and saving valuable bandwidth. This automation decreased on-call queries by 30% and boosted the efficiency of non-engineering teams.

MicroservicesREST APIsDatabase Optimization