Backend Software Engineer | Distributed Systems & Cloud Platforms | Ex @ Amazon Web Services (AWS)
Platform-focused backend software engineer with 5+ years of experience building and operating cloud-native, distributed systems at scale, most recently at Amazon Web Services (AWS).
Own and maintain a petabyte-scale internal data lake powering data science use cases across the company; led architecture, ingestion, and operational improvements as the sole engineer on the team.
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Migrated ETL logic from 50+ AWS accounts into a centralized pipeline, reducing latency by 40% and cutting support overhead by 60%.
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Automated AWS region onboarding using Lambda, CDK, and CloudFormation, enabling real-time ingestion from newly launched regions.
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Reduced ingestion infrastructure by consolidating scripts and pipelines into unified, Dockerized packages with multi-entry points.
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Led weekly operations reviews, presented infrastructure gaps to leadership, and drove sprint planning to close action items and reduce ticket load.
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Authored operational runbooks that cut investigation times from hours to minutes and improved operator self-service during incidents.
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Built secure, large-scale ingestion tools using AWS STS, Secrets Manager, KMS, and DynamoDB for both production and customer-facing use cases.
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Mentored teammates, improved system reliability, and handled high-severity on-call issues for a distributed log processing system.
Designed and implemented an end-to-end pipeline utilizing web scraping tools using both structured and unstructured medical records and clinical trials (EU, FDA, AU-NZ) data to synthesize the world’s biomedical knowledge into a concise database.
Build and monitored two Natural Language Processing (NLP) Models with named entity recognition to extract entities from medical documents using state-of-the-art Python Spacy library with an accuracy of 80% in entity recognition and validated the model performance by creating various data buckets based on the cosine similarity of the word vectors from the model output.
Developed custom scraping functions based on business stakeholder requirements to create custom daily feeds and alerts.
