Ameen Ashadhullah M

Software Engineer specializing in high-performance backend infrastructure, distributed data platforms, and serverless cloud architectures. I build resilient systems and automated pipelines that process over 100 billion records with zero data loss.

See My Resume 2x AWS Certified

My Superpowers

Distributed Data Platforms

Architecting high-throughput ETL/ELT pipelines processing 100B+ financial records with zero data loss.

Building Change Data Capture (CDC) streaming architectures using PostgreSQL logical replication and Debezium.

Managing petabyte-scale data lakehouses using Apache Iceberg, Hive metastores, and EMR Serverless.

Serverless & Cloud Infrastructure

Designing highly concurrent, event-driven microservices using AWS Lambda, SQS, and API Gateway.

Implementing self-healing, vertical autoscaling solutions for ECS Fargate to dynamically handle traffic spikes.

Automating cloud infrastructure provisioning and CI/CD pipelines across multiple AWS regions.

Observability & AIOps

Developing LLM-powered AIOps agents (Node.js/Claude) to autonomously triage system alerts and execute runbooks.

Centralizing enterprise telemetry by migrating to the ELK Stack using Firehose and Fluent Bit sidecars.

Ensuring 99.9% uptime through comprehensive automated testing, anomaly detection, and Sev-1 incident routing.

Skills / Certifications Core competencies

→

Experience Career journey

→

Projects Featured work

→

Education & Certifications

Education

🎓

Amrita Vishwa Vidyapeetham

B.Tech in Computer Science and Engineering (Artificial Intelligence)

Coimbatore, India | 2020 – 2024

Certifications

AWS Certified Data Engineer - Associate

- Amazon Web Services

AWS Certified Developer - Associate

- Amazon Web Services

Stanford University

Supervised Machine Learning: Regression & Classification

- Stanford / DeepLearning.AI

AWS Knowledge: Cloud Essentials

- Amazon Web Services

Core Competencies

💻

Languages

Python Java SQL Node.js Unix Shell

🔌

Backend & APIs

RESTful APIs AWS API Gateway Microservices System Architecture

🌊

Big Data & Streaming

Apache Spark Apache Kafka Debezium (CDC) Apache Iceberg Hive

☁️

Cloud Infrastructure

AWS Lambda EMR Serverless ECS Fargate EventBridge SQS Athena

🗄️

Databases

PostgreSQL Amazon S3 NoSQL

⚙️

DevOps & Reliability

ELK Stack Terraform (IaC) Docker CI/CD (TeamCity) Fluent Bit

Next Up

View Work Experience →

Work Experience

High-Impact Engineering Roles

A highly driven Cloud Data Engineer with a proven track record of architecting and building scalable backend systems capable of processing 100+ billion records. Specialized in designing robust ETL/ELT pipelines, modernizing cloud infrastructure, and orchestrating complex workflows across multiple distributed databases to drive enterprise efficiency.

See Experience

              Feb 2024 - Present
              Software Engineer (Data & Infrastructure)
              Guidewire Software
            
Architected the migration of a legacy data validation engine to a highly concurrent AWS Serverless stack (Athena, Lambda, S3), reducing overall infrastructure costs by 80%.
Engineered a highly available Data Integrity Service processing over 3 billion daily records, ensuring zero data loss across distributed PostgreSQL and Kafka pipelines.
Developed an autonomous, LLM-powered AIOps agent (Node.js, AWS Bedrock) that triages data gaps, analyzes ECS logs, and delivers root-cause summaries to Slack, saving 15+ manual engineering hours weekly.
Designed a dynamic data lakehouse ingestion pipeline utilizing EventBridge, SQS, and EMR Serverless (Spark) to consolidate hundreds of Iceberg source tables.
Implemented an SQS-backed smart throttling mechanism for Lambda and Athena, capping concurrent jobs at 25 to eliminate query timeouts and achieve a 99.9% job success rate.

              Python
              Apache Spark
              AWS EMR
              Kafka
              Apache Iceberg
              PostgreSQL
              ELK Stack
              ECS Fargate
            

              Sep 2023 – Jan 2024
              Software Engineer Intern
              Moopai
            
Designed and implemented analytical extraction workflows using Python and SQL.
Optimized complex caching mechanisms to achieve sub-millisecond broadcast latency in high-throughput data environments.

              Python
              SQL
              Data Workflows
              Performance Optimization
            

See My Work

View My Projects →

Featured Projects

Cloud Architecture & Data Engineering

Production-grade systems demonstrating real-time anomaly detection, distributed data platforms, and automated sentiment pipelines. These projects highlight my expertise in building scalable, highly-resilient architectures that process massive data streams efficiently.

See Projects

Real-Time Stock Analytics & Anomaly Detection Platform

Architecture: Engineered a serverless, event-driven web application utilizing AWS API Gateway, AWS Lambda, and Amazon Timestream for low-latency, time-series data storage.
Core Logic: Ingests live market data streams via the Twelve Data API. Features a custom anomaly detection engine that continuously evaluates market fluctuations against user-defined thresholds.
Impact: Achieved real-time data processing and automated alerting, dispatching instant notifications via Amazon SES when critical market anomalies are detected.

AWS Timestream AWS Lambda Amazon SES AWS API Gateway Twelve Data API React.js

LLM-Powered AIOps Auto-Remediation Agent

Architecture: Built an autonomous incident response agent deployed on AWS Lambda, integrated heavily with AWS EventBridge and the ELK Stack.
Core Logic: Intercepts system anomalies, queries container error logs, and executes diagnostic runbooks via Amazon Athena. Utilizes AWS Bedrock (Claude) to analyze internal documentation and logs to determine root causes.
Impact: Drastically reduced manual engineering triage time by generating comprehensive root-cause summaries and automatically routing them to Slack channels.

Node.js AWS Bedrock (Claude/LLMs) ELK Stack AWS Lambda EventBridge Amazon Athena

Interactive Election Analytics & Constituency Platform

Architecture: Architected a full-stack geographic data application utilizing PostgreSQL with PostGIS extensions to manage and query complex spatial and regional data.
Core Logic: Engineered a highly accurate constituency finder algorithm that instantly maps user locations to specific electoral districts, paired with an interactive swipe-card interface for data consumption.
Impact: Successfully transformed dense, state-wide demographic and electoral datasets into an accessible, gamified, and high-performance interactive application.

PostgreSQL PostGIS (Spatial Data) Python REST APIs React.js UI/UX Architecture

Let's Talk

Get In Touch →

Let's build together

Get In Touch

I am always interested in discussing high-scale backend architecture, distributed data platforms, or new engineering opportunities. Feel free to reach out!

Download Resume Send Email ✉️