Ruchika Zodage

Sole QA architect on a greenfield AI-driven culinary application. Made all framework design decisions
independently — evaluated multiple toolchains and defined the full testing strategy covering mobile UI, backend
API, schema validation, security, and CI/CD integration.
• Chose Appium + Pytest with POM architecture over a simpler script-per-test approach after evaluating 3
frameworks — the added upfront investment reduced estimated future maintenance overhead by ~40% and
enabled seamless Android/iOS cross-platform execution from a single codebase.
• Designed a Node.js API test runner covering 290+ scenarios across 28 endpoints; deliberately separated
functional, auth, boundary, and latency concerns into independent test layers so failures would pinpoint root
cause without manual triage.
• Introduced AJV-based OpenAPI schema validation as a contract-testing layer — a deliberate shift-left
decision that moved backend regression detection from staging to pre-deployment, eliminating a class of
defects that had previously required full regression cycles to surface.
• Evaluated AWS Device Farm vs. local device lab for real-device testing; selected Device Farm to eliminate
device procurement costs and enable parallel execution across OS versions, integrated via GitHub Actions
CI/CD pipeline.
• Identified the top 3 highest-risk user flows (Authentication, Profile Onboarding, Home Screen) using risk
based prioritisation, ensuring 100% automated coverage of paths that would cause immediate user drop-off
if broken.
• Designed security test plan targeting OWASP Top 10 vectors — prioritised SQL injection, XSS, and header
manipulation as the most exploitable surfaces for an API-first app, catching 2 critical vulnerabilities before
production deployment.

Led QA delivery for a regulated insurance platform handling claims and policy management. Responsible for test
strategy, defect governance, and security assessment across desktop and web systems.
• Prioritised security and vulnerability assessment as the first automation workstream — reasoning that a
breach in an insurance platform carries far higher business risk than a functional regression; built a
BurpSuite-based scan workflow covering SSL/TLS misconfiguration and OWASP Top 10 vectors.
• Chose TestComplete over custom Selenium scripts for desktop automation after evaluating team capability
and timeline constraints — its record-and-playback foundation allowed the team to reach coverage targets
30% faster while I built the strategic test architecture in parallel.
• Implemented a build verification (BVT) protocol as a gate before full regression runs, reducing wasted QA
cycles on broken builds and contributing to a 20% reduction in release defects.
• Governed the full defect lifecycle — introduced severity/priority triage standards that reduced critical bug
escape rates and improved stakeholder confidence in release readiness.

Led a 4-person QA team for a high-traffic mobile application. Responsibilities spanned team mentoring, test
strategy, sprint planning, and framework ownership.
• Restructured the QA team from ad-hoc manual testing to a structured automation-first model — defined skill
gaps, assigned ownership areas, and introduced sprint-level QA planning, reducing last-minute defect
discovery by an estimated 35%.
• Built a Pytest-based API automation framework from scratch rather than adopting an existing internal tool
— chose Pytest for its extensibility and native Python ecosystem fit, enabling faster regression cycles and
reducing manual API verification effort by ~30%.
• Stepped into a Business Analyst role during requirements gathering when the PM-Dev gap was causing
scope misalignment; facilitated structured requirement sessions that reduced mid-sprint requirement
changes by ~25%.
• Managed defect lifecycle in Azure DevOps — established triage rules and SLA targets for bug resolution
that improved fix turnaround time and gave leadership clear visibility into release health.

Embedded QA engineer for a major UK banking client. Primary focus on enabling independent test execution
through service virtualisation and banking API validation.
• Advocated for and implemented service virtualisation using DevTest Workstation to decouple test execution
from live banking dependencies — this enabled the QA team to run full regression suites without waiting for
environment availability, supporting continuous delivery pipelines.
• Built and managed a library of WSDL, Swagger, and RATL stubs — designed stubs to simulate both happy
path and fault-injection scenarios, improving defect detection for edge cases that were difficult to reproduce
in shared environments.
• Used Wireshark for RATL stub traffic analysis — a deliberate debugging tool choice that reduced stub
misconfiguration investigation time and improved troubleshooting accuracy for banking protocol issues.
• Optimised PeopleSoft CRM/HCM test processes and achieved 100% data accuracy during record migration
by designing a systematic validation matrix covering all critical data fields before go-live.
• Improved defect response time by 30% by introducing structured ticket resolution workflows and
environment-specific defect categorisation in ALM Octane.

Automation engineer on a complex PLM platform for a global automotive client. Responsible for building UFT
automation suites and managing testing across three concurrent product releases.
• Designed a UFT automation strategy that prioritised coverage of high-churn UI components — focused
automation investment where regression risk was highest rather than automating uniformly, achieving 40%
higher test coverage with proportionally less script volume.
• Reduced manual testing hours by 30% by identifying and automating the 20% of test scenarios that
consumed 80% of manual execution time — applied effort-vs-risk analysis to sequencing automation
backlog.
• Configured and deployed Teamcenter on enterprise servers for test environments — owning the
environment setup reduced dependency on DevOps teams and cutting environment-related test delays by
40%.
• Cut defect resolution time by 40% by implementing structured Polarion tracking with root-cause tagging,
enabling the development team to identify recurring failure patterns across releases.