Bhushan Ashokrao Bende

Senior Software Engineer | Senior Data Engineer

Senior Software / Data Engineer specializing in real-time streaming systems, distributed architectures, and cloud-native platforms. Expert in Kafka, Apache Flink, and AWS, with experience building petabyte-scale data pipelines and high-performance APIs.

Technical Skills

core

System Design
API Development
Database Design
Cloud Architecture

technologies

Node.js
Python
Go
Docker

databases

PostgreSQL
MongoDB
Redis
Elasticsearch
SQL
BigQuery

infrastructure

AWS
GCP
Kubernetes
CI/CD
Terraform

Security

OAuth 2.0
JWT
API Security

Monitoring & Observability

ELK Stack
Prometheus
Grafana
AWS CloudWatch
NewRelic

Data Engineering Streaming

Apache Kafka
AWS Kinesis
Apache Flink
Apache Spark / PySpark
Apache Beam
ETL / ELT Pipelines

API & Backend Development

FastAPI
Flask
API Security (JWT, OAuth, mTLS)
Zappa (Serverless Deployment)

DevOps & CI/CD

Git
GitHub Actions
Jenkins
AWS CodePipeline

Testing & Automation

Selenium WebDriver
pytest / unittest
Cucumber (BDD)
TestNG / Mocha
JMeter (Performance Testing)
Postman

Architecture & Concepts

Event Sourcing
CQRS
Async Processing
Distributed System Design
Fault Tolerance & Recovery
Data Modeling & Schema Evolution

Domain Expertise

Capital Markets
Payment Gateways & Transaction Processing
Financial Systems & Settlement Flows
Data Governance & Compliance

System Architecture Projects

Distributed Cache System

Implemented a distributed caching system supporting 100K+ concurrent users, Bloom Filter In Redis/ElasticCache : Deduplication Logic For Media Article

Tech Stack:

PythonRedisDockerElasticCacheAWS

Key Metrics:

  • 99.99% uptime
  • < 5ms latency
  • 50% cost reduction

Real-time Analytics Pipeline

Built streaming data pipeline processing 1TB+ data daily

Tech Stack:

PythonKafkaElasticsearchpyFlinkpyBeamKinesisKubernetesKinesis

Key Metrics:

  • 5M+ events/day
  • Sub-second latency
  • 99.9% accuracy

Elasticsearch Cluster Setup In Kubernetes | ECK

Architected and managed Elasticsearch clusters on Kubernetes with tailored compute and storage configurations to meet diverse workload and performance requirements. Optimized cluster performance for large-scale data ingestion and search by fine-tuning resource allocation, indexing strategies, and shard distribution. Designed scalable and resilient Elasticsearch infrastructure leveraging Kubernetes for orchestration, auto-scaling, and fault tolerance. Maintained performance SLAs across multiple clusters handling high-volume data and query workloads.

Tech Stack:

ElasticsearchKubernetesECK OperatorYamlKind K8FluxCDEC2CPU MetricsIngressZone Aware ShardsIndex ManagmentILMK8 Cron-Jobs

Key Metrics:

  • PetaByte Storage
  • Storage Classes
  • Index And Data Retrival Performance

Data Lineage | Kafka

Architected a data lineage framework to track end-to-end data flow across microservices by capturing metadata events into Kafka topics. Designed metadata schemas capturing processing stage, timestamps, and error states enabling full visibility into data lifecycle. Implemented event-driven lineage tracking where each microservice publishes processing metadata for observability and traceability. Enabled real-time monitoring and debugging of data pipelines by capturing failure points and processing delays within the lineage system. Integrated lineage data with AWS Athena allowing teams to query, analyze, and audit data flow across distributed systems.

Tech Stack:

KafkaAWS LambdaPythonAWS AthenaAWS Data-Catlog

Key Metrics:

  • 5M+ events/day
  • Sub-second latency
  • 99.9% accuracy

ETL Pipeline

Architected and implemented an end-to-end ETL pipeline on AWS using ECS, Kinesis, and Lambda to ingest and process data from multiple third-party APIs. Designed an API polling system on ECS to continuously fetch data from hundreds of external paid APIs and stream it into Kinesis for real-time processing. Built event-driven data processing workflows using AWS Lambda to transform, enrich, and route streaming data to Elasticsearch. Handled diverse data sources with custom transformation logic ensuring consistent data quality and schema alignment. Enabled scalable and fault-tolerant ingestion of high-volume data streams supporting near real-time analytics and search use cases.

Tech Stack:

AWS ECSAWS KINESISAWS LambdaElasticCacheDynamoDBS3

Key Metrics:

  • 5M+ events/day
  • Sub-second latency
  • 99.9% accuracy

Work Experience

Lead Consultant AWS | Big Data Operation | AI And Data

Wuerth-IT
March-2026 - Present
  • Engineered cloud-native data platforms on AWS capable of processing petabyte-scale data with high availability and fault tolerance.
  • Designed distributed streaming architecture integrating Kafka (MSK), AWS Kinesis, and Apache Flink for real-time data processing.
  • Orchestrated containerized workloads on Kubernetes (EKS) with auto-scaling using KEDA and Karpenter.
  • Built serverless architectures using AWS Lambda and API Gateway reducing infrastructure overhead and improving scalability.
  • Developed resilient, event-driven systems with asynchronous processing using Redis and message brokers.
  • Implemented end-to-end CI/CD pipelines enabling faster, reliable, and automated production deployments.
PythonDynamoDBDockerAWSCDKDatabricksKubernetesDistributed StorageDistributed ComputingRedisFastAPIPySparkFlink

Senior Software Engineer

Onclusive
Oct-2020 To March-2026
  • Engineered and scaled a real-time data platform processing petabyte-scale data and millions of articles daily using Kafka (MSK), Apache Flink, and Apache Beam.
  • Designed and implemented distributed data pipelines on AWS leveraging Lambda, Kinesis, S3, and ECS ensuring high throughput and fault-tolerant processing.
  • Built and managed Elasticsearch clusters on Kubernetes (EKS) handling PB-scale indexed data with optimized storage, scaling, and query performance.
  • Developed high-performance FastAPI-based microservices to expose and distribute processed data across multiple downstream teams and applications.
  • Containerized and orchestrated services using Docker and Kubernetes, enabling scalable and resilient deployments across environments.
  • Implemented event-driven and asynchronous processing architectures using Kafka and Redis to handle large-scale streaming workloads.
  • Led modernization of data pipelines improving performance, scalability, and cost efficiency by transitioning to Flink and Beam-based architectures.
  • Managed production-grade systems handling millions of events daily with strong focus on reliability, monitoring, and observability.
  • Played a key role in major production releases ensuring system stability, data accuracy, and high product quality.
  • Collaborated across teams to design data models, optimize pipelines, and ensure seamless data delivery for analytics and ML use cases.
PythonElasticsearchKubernetesDockerRedisKafkaKibanaAWSEC2 - ManagementAWS LambdaAWS KinesisS3ServerlessReal Time Data StreamingBatch ProcessingPostgreSQLKafkaELK StackGCP

Software Engineer

TSYS
May-2021 To Oct-2022
  • Developed API chaining architecture for payment transaction systems enabling seamless request flow across multiple downstream services in the Unified Commerce Platform (UCP).
  • Designed and validated complex request orchestration pipelines where transactions were transformed and routed across multiple APIs ensuring high reliability and data integrity.
  • Worked extensively with Apigee for API management, routing, and gateway-level transformations for secure and scalable API exposure.
  • Leveraged Google Cloud Platform (GCP) services including Compute Engine and BigQuery for data processing, storage, and transaction analytics.
  • Built and optimized API flows handling high-volume financial transactions with strict latency and consistency requirements.
  • Developed a robust automation validation framework for functional and regression testing of 100+ APIs improving test coverage and release confidence.
  • Implemented automated API testing using Postman, JMeter, and Node.js-based frameworks ensuring performance and reliability of critical payment systems.
  • Collaborated with cross-functional teams to ensure smooth integration between payment gateways, merchant systems, and downstream services.
  • Actively participated in release cycles, defect tracking, and production validation ensuring high-quality delivery of payment solutions.
GCP APIGEEBigQueryJavascript

Software Engineer

Altimetrik
Jan-2021 To May-2021
  • Build Analytics Dashboard for High Valued Payments
  • High Value payet are those where big instituion make transfer of Huge Amount they are carried out through different channel we need to monitor if anything fails with any reason so Operator can take appropriate action
  • Worked On Automation and Test Coverage Inprovement
JavaSQLSeleniumETL

Software Engineer

Cognizant
Jun-2018 To Jan-2021
  • worked on Testing and Automation for Banking Project for Trade Booking System
  • SFTR Regulation Implementation
  • Build and Tested Securities Lending And Transaction Execution Application
JavaSQLSeleniumETL

Contact

bhushanbende1996@gmail.com+91 9405708908