qperjkee

Software Engineer with commercial experience building and maintaining production services and large-scale data-processing pipelines in Python. Skilled in FastAPI, PostgreSQL, SQLAlchemy, Docker, Kubernetes, and asynchronous Python, with hands-on experience in backend development, data processing, feed integration, automation, and production reliability.

Focused on code quality, testing, and data reliability, with experience in unit and integration testing with Pytest, feed validation, data consistency checks, and regression testing. Experienced in production monitoring and observability with Grafana and Kibana, structured logging, alerting, incident investigation, and LLM-based automation and prompt engineering for engineering workflows.

Strong foundation in OOP, SOLID principles, data structures, algorithms, and software architecture, with experience owning services end-to-end - from development and deployment to monitoring, incident investigation, performance optimization, and reliability improvements. 

Skills

Backend Development: Python, FastAPI, REST APIs, Asynchronous Python, Asyncio, SQLAlchemy, Pydantic

Data & Automation: Data Processing, ETL Pipelines, Pandas, NumPy, Web Scraping, Data Extraction, Scrapy, Playwright, BeautifulSoup, LLM-based Automation, Prompt Engineering

Databases: PostgreSQL, MySQL, SQLite, MSSQL, SQL

Cloud & DevOps: Docker, Kubernetes, Linux, GitLab CI/CD

Testing & Observability: Pytest, Grafana, Kibana, Structured Logging, Monitoring, Metrics, Alerting

Software Engineering: OOP, SOLID Principles, Software Architecture, Performance Optimization, Production Reliability, Incident Investigation

Version Control & Integration: Git, GitLab, REST API Integrations, Slack, Jira, PagerDuty

Highlights

• Developed, deployed, and maintained production services and data-processing pipelines using Python, PostgreSQL, FastAPI, Docker, Kubernetes, and GitLab, ensuring reliable and scalable data aggregation.
• Onboarded 100+ new data sources by analyzing and mapping XML, JSON, and HTML feeds, handling edge cases, dynamic content, access restrictions, and anti-bot protection, and integrating structured data into the aggregation pipeline.
• Optimized indexing budget through CPC-based filtering for five high-volume projects, reducing indexer load by \~3.96M jobs/day and decreasing the US database footprint by 2.5–3M UIDs daily, resulting in faster indexing and more efficient database maintenance.
• Implemented LLM-based workflow automation for XML/JSON feed analysis, configuration generation, data processing, and repetitive operational tasks, reducing manual effort and accelerating data-source onboarding.
• Built and maintained workflow automation solutions integrating Slack, Jira, PagerDuty, and internal APIs, streamlining operational processes, alerting, monitoring, and SLA-driven incident management.
• Optimized production aggregation services, delivered production hotfixes, and improved system reliability and performance. Implemented retry and failure-handling mechanisms for data-processing workflows to improve resilience against transient failures.
• Monitored production systems using Grafana and Kibana, maintained structured logging, investigated production incidents, and improved service observability through metrics, dashboards, and alerting.