Emmanuel Davidson

Emmanuel K. Davidson

Platform Engineering Leader and Cloud & Data Architect working across four domains: Platform Engineering, Data Platform Engineering, Agent Systems, and Applied Analytics. Fullstack application engineering is the delivery vehicle across all four — the UIs, dashboards, and APIs each domain depends on.

Connect on LinkedInView projects

What I build now

My current work spans Rusha platform systems, TierraViva AI as a biodiversity intelligence platform, agent systems with orchestration and gateway infrastructure, and applied analytics products that put live data in front of operators and decision-makers.

What I bring to a team

I bring platform thinking, delivery discipline, operational visibility, and cost-aware AI experience to teams building complex systems.

  • System design, platform boundaries, and operating models
  • Build and release workflows for services, pipelines, UIs, and tooling
  • Deployment controls, GitOps delivery, and environment safety
  • Observability for runtime health, change visibility, and faster response
  • Reliability guardrails, approval paths, and secure execution
  • Scaling policy, infrastructure efficiency, and cost-aware growth

What the work delivers

The work delivers lower operating cost, elastic analytics capacity, safer delivery systems, and AI-assisted workflows with approval and observability built in.

  • 60–80% networking cost reduction on OWA infrastructure
  • Elastic Dremio executor scaling with query performance preserved
  • Production agent systems running on Kubernetes with approval controls
  • 7+ years experience across platform, data, and software systems

Where it has been built

One World Analytics: Senior Software Engineer & Platform Architect (August 2023 – Present) · The Jitu: Software Engineer III (Architecture Ownership & DevOps) (June 2021 – June 2023) · Touch Inspiration: Backend Developer (March 2019 – May 2021)

Domains & Tools

My work breaks down into four domains of expertise. Each domain has a curated set of tools, and each tool has a specific role inside that domain. Fullstack application engineering shows up inside every domain as the delivery vehicle for the UIs, dashboards, and APIs that domain depends on.

Platform EngineeringExplore →

Platform systems that make software easier to ship and safer to run — service boundaries, release workflows, deployment controls, environment isolation, and operational guardrails, with fullstack delivery as the vehicle for the platform's own APIs, UIs, and log-streaming surfaces.

Kubernetes — container orchestration & multi-tenant workload isolationArgoCD — GitOps-driven delivery and release rolloutsKEDA — event-driven autoscaling for stateful and event-driven workloadsDocker — containerization for portable buildsSealed Secrets — encrypted secret management in GitAmazon Cognito — auth and token refresh for platform APIsTraefik + cert-manager — ingress and TLS automationKaniko — in-cluster image buildsGitHub Actions — CI/CD pipelinesTerraform — infrastructure as codeAnsible — configuration automationGitHub Container Registry (GHCR) — container registryNestJS · React · TypeScript · gRPC · WebSocket — fullstack delivery (APIs, UIs, log streaming)

Data Platform EngineeringExplore →

The layer that carries analytics from ingestion to delivery — orchestration, compute, metadata, warehouse layers, observability, and scaling controls working together as one production system, with fullstack delivery for the analytics APIs, dashboards, and serving surfaces.

Apache Airflow — pipeline orchestrationdbt — data modeling and transformationApache Spark — distributed compute and processingDelta Lake — transactional storage and warehouse layerDremio — serving/query engine (with KEDA-driven elastic autoscaling)Databricks — ML and analytics computeGreat Expectations — data quality validationPostgreSQL — relational storeKEDA — event-driven executor autoscalingAmazon EKS — Kubernetes-managed analytics infrastructureArgoCD (GitOps) — infrastructure-as-code delivery for the data stackFastAPI · React · TypeScript · WebSocket — fullstack delivery (analytics APIs, dashboards, serving surfaces)

Agent SystemsExplore →

Agent systems as production infrastructure that can be deployed, observed, governed, and improved — multi-agent orchestration, model gateways, runtime packaging, control surfaces, approval paths, observability, and domain-specific workflows, with fullstack delivery for the agent control surfaces and dashboards.

Hermes — agentic runtime frameworkNATS JetStream — agent state and specialist routingLLM (OpenAI, Ollama) — model gateway and reasoning coreSlack API — human-in-the-loop approval workflowsKubernetes — agent deployment and schedulingKEDA — event-driven agent autoscalingPython — agent and runtime implementationFastAPI · React · TypeScript — fullstack delivery (agent control surfaces, profiles, dashboards)

Applied AnalyticsExplore →

Where the underlying platform becomes usable — dashboards for operators, APIs for products, assistants for exploration, and reporting surfaces that turn reliable data and AI systems into decisions people can act on, built with fullstack delivery.

React — dashboard and reporting UIsFastAPI — analytical APIs and serving surfacesWebSocket — live data streaming surfacesPower BI — executive BI reportingGrafana — operational dashboardsdbt — modeled analytics layersDremio — query layer for analytical surfacesTypeScript · Python — fullstack delivery (analytical product surfaces)

Project evidence

Platform

Rusha

Production platform system for building, validating, deploying, and operating software on Kubernetes. Combines GitOps-driven delivery, event-driven application architecture, multi-tenant namespace isolation, in-cluster builds, deployment templates, shared operational guardrails, and VCS integration across API, UI, and service layers. Serves the Platform Engineering domain.

AI

Agentic Systems

Production agent systems spanning multi-agent orchestration, domain-specific research agents, runtime-backed workflows, profile distributions, and model-gateway infrastructure. The work includes Kubernetes-based deployments, NATS-backed specialist routing, OpenAI-compatible and Ollama-facing gateway layers, approval paths, observability, and domain workflows for biodiversity, policy, and analytical operations, with Hermes used as one runtime within that stack. Serves the Agent Systems domain.

Data

TierraViva AI

Biodiversity intelligence platform that ingests and mines policy, biodiversity, patent, and research data, processes it through GitOps-managed cloud data infrastructure, serves it through APIs and web interfaces, and makes it available to agents for analysis, navigation, reporting, and decision support. Its implementation spans document and research corpora, ETL and orchestration systems, Spark and analytics infrastructure, serving layers, and agent-consumable knowledge workflows. Serves the Data Platform Engineering and Applied Analytics domains.