Maxwell Edwards

Architecture: Serverless application (AWS Lambda) sitting behind a proxy layer, deployed through a Jenkins pipeline with consumer-driven contract tests (Pactflow) verifying compatibility between the proxy and application layers on every change, backed by unit and integration test suites and Mend Renovate for automated minor-version dependency updates.

Challenge: The application manages elevated-access privileges on ~400 Linux servers across the US and UK, a high-risk surface where undetected breaking changes or unpatched dependencies carry outsized security risk. Before the rebuild there were no integration tests, and vulnerability remediation was almost entirely manual.

Design Decisions & Tradeoffs: Adopted consumer-driven contract testing specifically to guard the proxy-to-application-layer boundary, catching breaking changes before they reached production rather than relying on manual regression checks. Paired this with Mend Renovate to auto-remediate minor version bumps continuously, shrinking the vulnerability backlog proactively instead of batching upgrades reactively.

Scalability & Performance: Manages roughly 400 Linux servers across US and UK environments, with deployments shipping multiple times per week and zero production incidents in the 6 months since the new pipeline went live.

Architecture: New Kubernetes (on-prem) and Azure application built around a precomputation engine: scoring logic runs ahead of request time across all employees and skills, persisting results to a dedicated table that the API reads directly at login instead of computing scores live.

Challenge: The scoring algorithm had to reconcile inconsistent signals across five vendor platforms (SkillUp, LinkedIn, Udemy, HackerRank, and an internal source) plus Workday review data - accounting for whether a skill even had an associated test, how to weight tested versus untested evidence, and which review categories should most strongly nudge employees toward certification. At login, scores across 7,000 employees and roughly 650 skills needed to already be ready, ruling out real-time computation.

Design Decisions & Tradeoffs: Chose to precompute and store scores ahead of time rather than calculate them synchronously on each login, keeping page load fast regardless of algorithm complexity as vendor sources and weighting rules grew. Co-designed the weighting logic with stakeholders across all five vendor integrations and Workday to align on which signals should carry the most weight.

Scalability & Performance: Serves 7,000 employees across roughly 650 tracked skills and five external data sources, fully decoupled from request-time computation via the precomputation architecture.

Architecture: Hybrid pipeline: precomputed graph transformations offline and client-side progressive rendering for interactive exploration.

Challenge: Large, highly connected datasets caused UI stalls and backend query fan-out beyond 3 generations of connections.

Design Decisions & Tradeoffs: Applied graph partitioning and selective edge expansion to preserve usability without over-fetching irrelevant relationships.

Scalability & Performance: Optimized data structures and rendering techniques to maintain responsiveness as graph size increased with deeper relationship exploration.