Backend systems for Avis Budget Group.

I am employed by Tata Consultancy Services and work on the Avis Budget Group client engagement across reservation, fleet, data-processing, and production-support workflows. The work spans Java, Spring Boot, GraphQL, high-volume data movement, cloud delivery, and operational reliability.

Measured outcomes

Performance work with a before and after.

90K+records handled per pipeline run

50 → 20minutes after runtime optimization

9 GB → 900 MBprocessing footprint after Avro optimization

Engineering scope

The work behind reliable movement of data.

My contribution spans the path from ingestion and service logic through deployment and observability. The goal is not simply to process more, but to make the behavior easier to operate and trust.

High-throughput pipeline

Designed and owned Spring Boot data processing for 90K+ database records with bounded concurrency, retries, failure isolation, and GraphQL delivery.

Backend and data integration

Java, Spring Boot, GraphQL, PostgreSQL, IBM DB2, Oracle Database, Kafka, and Apache Avro across production service boundaries.

Delivery and observability

AWS, Kubernetes, Jenkins, ArgoCD, Prometheus, Grafana, API tracing, SQL debugging, and log analysis across deployment and support.

How I contributed

Find the constraint. Change the system. Verify the result.

Designed and owned a Spring Boot data pipeline processing 90K+ database records per run.

Reduced critical runtime from approximately 50 minutes to approximately 20 minutes.

Reworked the data flow around Apache Avro, reducing the processing footprint from approximately 9 GB to approximately 900 MB.

Diagnosed production behavior through API tracing, SQL debugging, log analysis, monitoring dashboards, and Kubernetes pod inspection.

Production stack

Technologies used across the engagement.

Java

Spring Boot

Kafka

Apache Avro

GraphQL

PostgreSQL

IBM DB2

AWS

Kubernetes

Jenkins

ArgoCD

Prometheus

Grafana

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