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.
