Shrihari K

Software Engineer

Bengaluru, Karnataka, India3 yrs 9 mos experience

Highly Stable

Key Highlights

Expert in building resilient backend systems.
Proven track record in cloud-native financial solutions.
Strong mentor with experience in async programming.

Stackforce AI infers this person is a Backend Engineer specializing in Fintech solutions with expertise in cloud-native architectures.

Contact

Skills

Core Skills

JavaSpring BootPysparkAws Dms

Other Skills

AWS ECS FargateDynamoDBSpring Cloud OpenFeignRedshiftSQLAWS GlueReact.jsC#Universal Windows Platform (UWP)Apache KafkaPython (Programming Language)Amazon Web Services (AWS)AWS LambdaAmazon S3Problem Solving

About

I build backend systems in Java and Spring Boot that handle real load without falling over. For the past 4 years at HashedIn by Deloitte, I've been embedded in a platform team modernising mission-critical financial infrastructure for one of the largest asset management companies globally. That's meant: • Migrating legacy COBOL programs into cloud-native Spring Boot microservices on AWS ECS Fargate - designing async REST APIs with ExecutorService and CompletableFuture that hold p95 latency under 300ms. • Building resilience into third-party integrations using Circuit Breakers, retry logic, and exponential backoff via Spring Cloud OpenFeign - cutting transient failure rates by 25–30%. • Designing DynamoDB access patterns with GSIs for high-throughput customer-facing workflows. • Modernising COBOL batch programs into PySpark pipelines on AWS Glue processing 30M records daily, completing in under 20 minutes. • Architecting an end-to-end ORDS data lake - AWS DMS for CDC ingestion, Redshift with SCD Type 4 history tracking, and automated alerting via SNS. I've led features end-to-end, driven architecture decisions, and mentored engineers on async patterns and AWS integration. Core stack: Java · Spring Boot · AWS (ECS Fargate, DynamoDB, Lambda, Glue, Step Functions, Route 53) · Kafka · PySpark · Python I'm selectively open to backend and data engineering roles at product companies where engineering quality is taken seriously.

Experience

3 yrs 9 mos

Total Experience

3 yrs 9 mos

Average Tenure

3 yrs 9 mos

Current Experience

Hashedin by deloitte

3 roles

Software Engineer 2

Promoted

Oct 2024 – Present · 1 yr 8 mos

Led backend development for a cloud-native financial platform working with one of the world's largest asset management companies.
Designed and owned async REST APIs in Java and Spring Boot (ExecutorService, CompletableFuture) handling tens of thousands of daily requests at sub-300ms p95 latency.
Led end-to-end development of 2–3 Spring Boot microservices deployed on AWS ECS Fargate, from design through production.
Built resilient third-party service integration using Spring Cloud OpenFeign with Circuit Breaker, retry, and exponential backoff - reducing transient failures by 25-30%.
Modelled DynamoDB table schemas with GSIs to support high-throughput, low-latency customer-facing access patterns.
Contributed to multi-region high-availability architecture using Amazon Route 53 active-active routing, supporting 99.99% uptime.
Established observability via Honeycomb dashboards tracking p95 latency, request volume, and error rates; built automated threshold alerts for stakeholder notification.
Mentored 2-3 engineers on async programming patterns, AWS service integration, and production debugging.

JavaSpring BootAWS ECS FargateDynamoDBSpring Cloud OpenFeign

Software Engineer 1

Promoted

Sep 2022 – Oct 2024 · 2 yrs 1 mo

Translated 4-5 high-complexity COBOL Business Rule Engines (thousands of lines) into validated PySpark transformations on AWS Glue - preserving exact business rule fidelity while eliminating mainframe dependency.
Built and owned CDC pipelines using AWS DMS for near real-time ingestion from Aurora DB into Redshift across 6-7 source tables.
Implemented SCD Type 4 in Redshift to maintain separate current and history tables - a deliberate choice over Type 2 to support downstream analytical queries without conflating record states.
Optimised PySpark batch jobs via broadcast joins and intermediate DataFrame caching, improving execution efficiency by 20–25%.

PySparkAWS DMSRedshiftSQL