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Jobin User

@jobinuser

I’m a backend software engineer building scalable Python microservices and high-performance APIs, improving latency and reliability.

United Arab Emirates

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What I'm looking for

I’m looking for a backend role where I can build scalable Python microservices and event-driven data pipelines, improve latency and reliability, and own production infrastructure with strong observability, testing, and mentoring.

I’m a Backend Software Engineer with 4+ years of experience building scalable backend systems, microservices, and high-performance APIs using Python, Django, and FastAPI. I’ve designed distributed systems and event-driven architectures with Kafka, with a proven impact that includes reducing API latency by 60% and supporting data processing platforms handling 10M+ records daily.

I focus on production-ready backend infrastructure: optimizing databases, improving reliability, and automating operations and monitoring. At Turbolab, I implemented DAG-based workflow orchestration to cut pipeline failures by 40%, built internal workflow automation to eliminate 30+ hours of manual work weekly, maintained 99.9% system uptime using Docker-based deployments, and strengthened code quality through reviews, testing (Pytest), and mentoring junior engineers.

Experience

Work history, roles, and key accomplishments

Current

Backend Software Engineer

Current

Turbolab Technologies

Apr 2022 - Present (4 years 1 month)

Designed scalable Python/Django microservices and data pipelines handling 10M+ records daily, cutting API latency by 60% and reducing response time from 500ms to 150ms through REST/GraphQL and SQL optimization. Built automation and event-driven pipelines with DAG-based workflow orchestration and Kafka, maintaining 99.9% uptime and reducing pipeline failures by 40%.

Python Django fastAPI Microservices REST APIs GraphQL SQL Optimization Kafka Workflow Orchestration (DAG)Docker

Python Developer (Data Team)

Turbolab Technologies

Apr 2021 - Apr 2022 (1 year)

Built resilient Python scraping systems using Playwright and Pyppeteer, increasing web extraction success rate by 25% and improving fault tolerance with retry/error handling. Automated data validation and ingestion pipelines, reducing manual effort by 50% and lowering workflow execution time and infrastructure usage through optimized scraping and transformations.