Onkar Kumbhar

Software Engineer

Pune, Maharashtra, India8 yrs 10 mos experience

AI EnabledAI ML Practitioner

Key Highlights

Reduced cloud costs from $150/day to under $10.
Engineered event-driven applications for cloud storage.
Led S3-based storage platform development with advanced features.

Stackforce AI infers this person is a Backend-focused Software Engineer with expertise in Cloud and IoT solutions.

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Skills

Core Skills

JavaCloudMicroservicesSoftware DesignData Management

Other Skills

Spring BootAmazon Web Services (AWS)Microsoft AzureProblem SolvingObject-Oriented Programming (OOP)AlgorithmsContinuous Integration and Continuous Delivery (CI/CD)Code DesignCode ReviewData StructuresAnalytical SkillsDatabasesSoftware InfrastructureREST APIsApache Iceberg

About

Software engineer with 8+ years of experience building backend systems and infrastructure platforms using Java and Spring Boot. Currently building an internal S3-based data platform with features like metadata search, event-driven notifications, and usage metering services. My work focuses on distributed systems, caching, system performance, and cloud-native architectures. Key areas of experience: Distributed caching with Redis Search infrastructure using OpenSearch / Elasticsearch Storage platform APIs on S3 Usage tracking and billing systems System design and production observability Experienced with AWS, Azure, Kubernetes, Terraform, and Datadog, and passionate about designing scalable systems that power developer platforms and internal infrastructure.

Experience

8 yrs 10 mos

Total Experience

7 yrs

Average Tenure

2 yrs 1 mo

Current Experience

Siemens digital industries software

4 roles

Advanced Software Engineer

Aug 2024 – Present · 1 yr 10 mos

Contributing to the development and enhancement of a core data lake system, a foundational service for various Siemens products.
Architected and implemented an effective caching layer to resolve excessive DynamoDB costs (previously $150-$200/day) during peak loads, successfully cutting daily expenditure to under $10 and improving system resilience.
Spearheading the migration of existing applications onto a new, modern platform architecture.
Responsible for managing and optimizing the cloud infrastructure supporting these critical applications

JavaSpring BootAmazon Web Services (AWS)Microsoft AzureProblem SolvingObject-Oriented Programming (OOP)+3

Software Engineer

Jun 2019 – Aug 2024 · 5 yrs 2 mos

Designed and developed robust microservices for the MindSphere Industrial IoT platform, deploying across multi-cloud environments (AWS & Azure).
Engineered and implemented an event-driven, serverless application to efficiently process cloud storage events.
Developed a system for capturing cloud storage usage in a complex multi-tenant environment using Quartz scheduler.
Improved the usability and reduced infrastructure management overhead for an AWS STS Token sharing API.
Implemented an Attribute-Based Access Control (ABAC) mechanism leveraging Elasticsearch for enhanced security and flexibility.
Significantly boosted API performance, reducing latency from 5 seconds to under 500ms, while concurrently decreasing database load.

MicroservicesCode DesignAlgorithmsCode ReviewData StructuresAnalytical Skills+4

Associate Software Engineer

Dec 2017 – May 2019 · 1 yr 5 mos

Played a key role in migrating Spring Boot-based microservices (part of Gateway, Identity Management, and Authorization systems) from AWS to the Azure platform.
Managed microservices deployment pipelines and cloud infrastructure components specifically on the Azure platform.
Actively participated in cloud cost optimization initiatives, contributing to savings exceeding 50%.
Automated internal processes and deployment tasks using Python/Shell scripting and GitLab CI/CD pipelines.

Graduate Engineering Trainee

Jul 2017 – Dec 2017 · 5 mos

Siemens foundational technologies

Advanced Software Engineer

May 2024 – Present · 2 yrs 1 mo

Data lake an S3-based storage platform API used by multiple teams — features included metadata search, event notifications, and secure ingestion. Led Apache Iceberg integration for table format support, enabling schema evolution and reducing analytical query times (Athena) from ~12 mins to seconds for common queries. Implemented production observability (Datadog + EventBridge) with dashboards and SLO-based alerts; served on-call duties and led incident response. Owned distributed caching strategy with Redis, reducing DynamoDB requests and lowering daily DynamoDB costs from ~$150–200/day to single-digit daily spend. Optimized OpenSearch deployment (6 × i3.4xlarge nodes) and performed index/retention strategies to cut file-service costs by ~50%.