Dalton T.

About Dalton T.

Senior Software Quality Engineer with 10+ years of experience owning quality, security, and delivery for backend, cloud-native systems serving millions of users.I specialize in Python-based automation, API and data validation, and building mechanisms that prevent defects and reduce operational toil.

Experience

1. Senior Software Engineer - Automation Platform (Ai)

   Ds4 Tech

   Oct 2021 — Present · Torrance, CA, US

   Test Tools Platform: Shipped a versioned Python + TypeScript/Node.js package/CLI that runs API/workflow validation in CI and scheduled canaries, with standardized configuration and developer documentation- Python APIs for Business Validation: Built and maintained internal Python REST APIs for self-serve eligibility/data checks used in end-to-end scenarios, reducing back-and-forth during UAT readiness- Results and Triage Pipeline: Parsed execution artifacts, published reports to S3, and posted actionable Slack summaries (failure signatures, failing steps, request/response diffs, owner routing) to accelerate debugging and reduce alert noise- AI Automation: Implemented LLM-assisted failure triage (signature clustering plus concise root-cause hypotheses) with human-in-the-loop routing and feedback signals to continuously improve classification quality- LLM Triage Service Hardening: Standardized the \"context packet\" sent to the LLM (error signature, relevant logs, request/response diffs, recent deploy metadata) and enforced structured outputs (summary, likely cause, owner suggestion, next checks) with trace IDs for audit and offline evaluation- Pandas ETL + Reporting: Built ETL pipelines (Pandas) to normalize run artifacts across systems and generate weekly trend reports for sprint reviews (flake rate, top failure signatures, MTTR, and regression hot spots)- Peer Reviews and Standards: Led code reviews and introduced reusable patterns (helpers, assertions, reporting conventions) to improve reliability and reduce review churn- Impact: Reduced triage time by ~45%; decreased flaky-failure noise by ~30%; improved regression detection latency from ~4 hours to ~20 minutes; increased automated coverage of critical workflows by ~25%- Mobile Automation: Maintained and expanded a Swift/Xcode-based iOS functional automation framework; improved stability and diagnostics with BrowserStack videos/screenshots.

Education

• Caroline University

  Master's degree, Computer Science

• Dom Feliciano

  Associate's degree, Computer Programming, Specific Applications

  2008 — 2009

• Universidade La Salle

  Associate's degree, Computer Software Engineering

  2009 — 2013