Work
Selected engagements demonstrating multi-disciplinary engineering across infrastructure, backend systems, and data platforms.
Government Infrastructure Modernization
Problem
Multi-cluster Kubernetes estate suffering deployment failures and operational risk. Critical services unreliable due to 8,000+ lines of brittle bash/Jenkins logic. Manual processes blocking release velocity. Compliance requirements for government security standards.
Approach
Replaced fragile bash/Jenkins workflows with robust Terraform-based GitHub CI/CD. Implemented algorithm-driven infrastructure modules for automated configuration. Integrated AWS Secrets Manager with external-secrets operator for compliance. Eliminated environment drift and security gaps through infrastructure-as-code.
Outcome
Reduced onboarding time and compliance risk significantly. Cut release delays and stability issues. Increased reliability of critical services by modernizing multi-cluster Kubernetes estate. Reduced deployment failure and operational risk through automated, compliant infrastructure.
Mission-Critical Telemetry Migration
Problem
Mission-critical embedded QNX telemetry system processing 4,000+ high-frequency CAN messages per second required cloud migration. On-premises infrastructure created single point of failure for live race broadcasts. Race-day failure risk unacceptable. Needed proof of cloud-native architecture feasibility.
Approach
Delivered proof-of-concept migration to AWS, validating cloud-native telemetry pipelines for broadcast data. Designed high-throughput ingestion architecture ensuring millisecond-level message processing. Demonstrated future-proofed infrastructure with lower operational risk and dependency. Enabled collaboration across embedded, DevOps, and software teams to meet tight race-season timelines.
Outcome
Proved scalability with high-throughput ingestion architecture in AWS. Reduced race-day failure risk by validating cloud-native telemetry pipelines. Enabled long-term resilience by demonstrating feasibility for full migration. Proved real-time processing of 4,000+ messages per second powering fan-facing features.
Supply Chain Intelligence Platform
Problem
SaaS platform requiring multi-tenant infrastructure. Complex data models for supply chain analytics. Frontend, backend, and infrastructure all needing coordinated development.
Approach
Full-stack development from infrastructure to UI. AWS architecture with multi-tenant isolation. Backend services for supply chain data processing. React frontend for analytics visualization. Three-tier Terraform for rapid environment provisioning.
Outcome
Production SaaS platform with secure multi-tenant architecture. Full-stack solution delivered without coordination overhead. Infrastructure-as-code enabling new environments in minutes rather than weeks.
Cloud IoT SaaS Platform
Problem
Industrial technology company needed market entry for embedded analytics SaaS. Required transition from on-chip analytics to scalable cloud platform—enabling remote management for devices without physical cabling dependency. Needed proof-of-concept to secure board-level funding.
Approach
Delivered proof-of-concept Cloud IoT platform demonstrating end-to-end IoT analytics on cloud-native stack. Proved technical feasibility with live demo to board-level executives. Demonstrated high performance with RESTful backend and time-series database ingesting terabytes per second. Provided cross-functional technical leadership across frontend, backend, and data architecture.
Outcome
Secured SaaS market entry for client by delivering project as scalable cloud delivery model. Secured post-PoC funding following successful board-level presentation. Demonstrated cohesive delivery across frontend, backend, and data architecture ensuring technical feasibility.
Fan Engagement Platform Transformation
Problem
Multi-million-pound fan personalisation initiative near failure. Release cadence at once every two months. Manual deployment processes creating operational risk. Multi-vendor teams lacking coordination and engineering standards. Critical fan engagement features blocked by infrastructure and delivery problems.
Approach
Established north star architecture across platform, backend, and data teams. Implemented GitOps-driven CI/CD with developer self-service infrastructure. Directed engineering strategy across multi-vendor teams spanning data lake architecture, AI/ML integration, and full-stack development. Embedded quality standards and technical leadership to stabilise failing initiative.
Outcome
Transformed release cadence from once every two months to 8+ deployments per day. Stabilised near-failing initiative through technical leadership and engineering standards. Accelerated team velocity with self-service infrastructure and automated pipelines. Enabled critical fan engagement initiatives through coordinated multi-disciplinary delivery.
CI/CD Visibility Platform
Problem
No visibility into CI/CD pipeline utilisation and performance trends. Senior leadership lacked data-driven insights for platform investment decisions. Operational improvements hampered by lack of analytics. Commercial monitoring tools costing thousands monthly while requiring only basic metrics and alerting.
Approach
Designed real-time CI/CD monitoring and alerting platform using Azure DevOps APIs, Lambda, DynamoDB, and API Gateway. Built executive dashboards surfacing pipeline utilisation trends and performance metrics. Implemented real-time alerting for incident detection. Delivered enterprise-grade monitoring at minimal operational cost.
Outcome
Executive visibility into CI/CD utilisation enabling data-driven investment decisions. Operational improvements through analytics identifying bottlenecks across the platform. Increased reliability via real-time alerting reducing incident response times. Cost efficiency achieving enterprise monitoring for under $1/month versus thousands for commercial alternatives.
Developer Platform & Automation
Problem
Inconsistent CI/CD patterns across teams reducing developer productivity. Hybrid system integration challenges slowing delivery velocity. Manual processes blocking automation of common workflows. Lack of reusable abstractions forcing teams to rebuild solutions repeatedly.
Approach
Developed internal Python SDK and CLI with reusable patterns for rapid REST API integration. Built abstractions for TeamCity projects, builds, and log ingestion enabling automated tooling. Integrated legacy system interfaces with modern automation workflows. Automated .NET pipeline discovery and reporting through regex and Jinja templates. Strengthened quality feedback loops with GitHub and Bitbucket PR workflow integration.
Outcome
Improved developer consistency and productivity through reusable class patterns. Integrated legacy and modern tooling eliminating manual coordination overhead. Increased pipeline reliability via automated discovery and reporting. Strengthened quality gates enabling PR comment injection for immediate feedback.
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