I am a backend software engineer with 5 years of experience building and shipping production systems across SaaS products on AWS. My work has focused on third-party integrations, large-scale automated data collection, and data-cleaning pipelines, from scrapers and ingestion to data-quality workflows.
I have led backend development for distributed systems and microservice architectures, including high-volume async workloads and real-time integrations. I enjoy solving performance bottlenecks, improving reliability, and designing systems that scale cleanly as product demands grow.
In my recent roles, I worked on health-inspection SaaS, AI training and coaching platforms, and energy cost-optimization software. I contributed to backend re-architecture, latency reduction, enterprise authentication, database hardening, and cloud infrastructure improvements across AWS-based environments.
A major part of my experience is automation and data engineering. I have built and maintained large scraper fleets, ingestion pipelines, matching and deduplication systems, geocoding and normalization workflows, and integrations with external services and client webhooks.
I am comfortable working across Python, SQL, and Go, and I regularly use Django, FastAPI, gRPC/Protobuf, Celery, Kafka, PostgreSQL, Redis, DynamoDB, Docker, and CI/CD tooling. I also have hands-on experience with Selenium, Playwright, ETL, and AWS services such as Elastic Beanstalk, Aurora RDS, Redis, and EC2.
I value clean architecture, measurable performance gains, and practical engineering decisions that reduce cost and improve uptime. I also enjoy mentoring teammates, reviewing code, and setting technical standards that help teams move faster with confidence.
Bachelor’s degree in Computer Science. Bishkek, Kyrgyzstan.
Re-architected a Django health-inspection backend around clearer domain models, reducing heavy database query time by 1–2 seconds. Ran ingestion for 500+ regional scrapers using Python/Selenium and Celery with canaries and retries. Built data-quality pipelines for matching, deduplication, geocoding, and normalization, improving accuracy to 85–95% and reducing LLM spend by 65%. Owned AWS delivery with Elastic Beanstalk, Aurora RDS, Redis, EC2, CI/CD, and Sentry, cutting monthly infrastructure cost by 25–30%.
Re-architected an OpenAI-powered real-time integration on WebRTC and WebSockets, reducing response latency from 5–7 seconds to under 2 seconds. Built gRPC/Protobuf transport for LLM evaluation across services, handling 4x more data at 30% faster execution. Led enterprise authentication on Entra ID with SSO, OIDC/SAML, MFA, and RBAC. Hardened database models and Alembic migrations, improving deployment speed, reducing DB errors, and increasing uptime toward 99.99%. Refactored a legacy Django/REST/gRPC async backend to remove cross-service blockers and reduce timeout issues.
Architected a cost-optimization platform evaluating 10K+ building scenarios and saving clients $500K+ in modeled long-term expenses. Introduced Kafka to distribute async third-party results to internal workers and client webhooks, improving operations 3x. Designed a modular AWS backend, offloading authentication to Okta with SSO and MFA and integrating third-party services to speed responses by 40%. Reduced database size by 30% by moving non-critical data to external storage services.
Led backend development for a distributed system of 15+ microservices across 20+ AWS services, replacing a monolithic single-server architecture. Built a core Python service for real-time, high-volume async workloads with 70–80% test coverage and 99.9% uptime. Scaled the system to 50K+ concurrent requests through event-driven async pipelines, database tuning, query optimization, and profiling. Built an automation platform integrating 100+ sites via custom Python scrapers with authentication and CAPTCHA handling, reducing manual work by 95%. Mentored a junior engineer and established architecture standards for the codebase.
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