Durvesh Narwelkar

Software Engineer

Pimpri Chinchwad, Maharashtra, India10 mos experience

Key Highlights

Built scalable data systems using PySpark.
Optimized data processing for enterprise-scale datasets.
Passionate about cloud technologies and backend systems.

Stackforce AI infers this person is a Data Engineer with a focus on Big Data and Cloud technologies.

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Skills

Core Skills

Data EngineeringBig Data

Other Skills

AWS Auto ScalingAWS Command Line Interface (CLI)AWS GlueAWS Identity and Access Management (AWS IAM)AWS LambdaAWS Load BalancerAWS VPCAmazon AthenaAmazon CloudWatchAmazon EC2Amazon QuickSightAmazon RedshiftAmazon S3Amazon VPCAmazon Web Services (AWS)

About

I’m an aspiring Cloud, DevOps, and Data Engineer, currently building a strong foundation through self-driven projects and continuous hands-on learning. As a student passionate about cloud computing, system design, and backend development, I enjoy exploring how scalable, secure, and reliable systems are built in the real world. I’ve gained practical experience through key projects and follow a project-first, problem-solving approach — picking up concepts by applying them directly, diving into documentation, breaking things, and learning by fixing them. I’m particularly drawn to roles where I can work with cloud technologies, automate infrastructure, and understand backend systems from the ground up. Currently Exploring: • Cloud-native design patterns • Infrastructure as Code (Terraform, Cloud Formation basics) • System reliability and observability • CI/CD fundamentals • Data Warehousing Platforms (AWS Redshift, Athena, Glue) • Python and Shell scripting

Experience

10 mos

Total Experience

10 mos

Average Tenure

10 mos

Current Experience

Quick heal

Software Engineer

Aug 2025 – Present · 10 mos · Pune, Maharashtra, India · On-site

Building a scalable data monitoring and analytics system using PySpark to process and visualize massive enterprise-scale storage data (hundreds of petabytes).
Key Highlights:
Designed a PySpark-based data pipeline to extract and aggregate metadata securely within on-premise infrastructure.
Optimized data processing for speed, scalability, and fault tolerance across large distributed datasets.
Integrated processed outputs with Apache Superset dashboards for real-time visibility into storage utilization and growth trends.
Worked independently on the core development and collaborated with the team for production rollout.
Tech Stack: PySpark · Python · SQL · Apache Superset · Linux

PySparkPythonSQLApache SupersetLinuxData Engineering+1