I build the platforms data runs on.

Pipelines, clusters, and warehouses that carry data from every source to the dashboards, analytics, and models people rely on.

Talk about your data

Trusted solutions,
built to be used.

I enjoy the unglamorous work that makes everything else possible. Behind every useful dashboard or model sits a set of pipelines, permissions, and infrastructure that someone had to get right, and that's where I like to spend my time.

I lean on containers, Kubernetes, and GitOps to keep systems scalable, portable, and reproducible, but I favour simple, well-understood tools and small reversible changes over big-bang rewrites. Good platforms make the right thing the easy thing.

Platforms teams
can ship on.

Platform services for engineering teams, from the cluster up: six parts of one system, each built as code and documented for the team that runs it.

  • Infrastructure as code

    Provisioning cloud infrastructure with Terraform to keep every environment reproducible, peer-reviewed, and free of drift.

  • GitOps and CI/CD

    Automating deployments with Argo CD and CI pipelines, making every release traceable, consistent, and easy to roll back.

  • Kubernetes platforms

    Building and operating production clusters on GKE and AKS, with RBAC, autoscaling, and safe upgrades, so workloads run reliably at any scale.

  • Networking and traffic management

    Exposing services securely through Gateway API, ingress, TLS, and WAF policies, balancing accessibility with protection.

  • Observability

    Wiring metrics, logs, and traces into Grafana, so teams see how their workloads behave and hear about problems before users do.

  • Security and access automation

    Replacing manual requests and long-lived secrets with Workload Identity, least-privilege roles, and self-service access workflows.

Data teams
can decide with.

Data services that take scattered sources to numbers people trust: ingested, governed, warehoused, and modelled, on a schedule that runs itself.

  1. Pipeline orchestration

    Reliable workflows with retries, dependencies, and alerting, so data arrives on time, every time.

  2. Ingestion and integration

    Data brought in from APIs, databases, files, and event streams, in batches or by change data capture, and landed cleanly and consistently.

  3. Data governance

    Role-based access and clear ownership of every dataset, so teams can use data confidently and compliantly.

  4. Cloud data warehouse

    Snowflake environments set up and run well: databases, compute warehouses that scale with the business.

  5. Transformation and modelling

    Raw data turned into trusted, analytics-ready models with dbt and SQL, with version control and documentation built in.

From first question
to production.

  1. Understand the data

    Map sources and owners, profile quality and gaps, and agree on the metrics that matter.

  2. Build the pipeline

    Model the warehouse, automate ETL/ELT with tests, and version everything in Git.

  3. Deliver insight

    Dashboards people actually use, monitoring and alerting, and documentation and handover.

Have data that needs
somewhere to go?

I'd love to hear about your roadmap and help turn it into reliable pipelines and clear insight.

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