From raw events to defensible decisions.

Data Engineering

We build data platforms that engineering and analytics teams trust. SSOT-first projection models, event spines, lookup-driven business rules, and the metadata plumbing that makes AI and BI cohabit. We pick tools your team can operate without a dedicated platform squad.

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Data Engineering — OTBX practice
DATA ENGINEERING · PRACTICEotbx://
37 migrations
Schema evolution under SSOT discipline
Idempotent
Webhook handlers with signature checks
One-canonical
Workflow from inquiry to closeout
Deliverables

What you get.

  • SSOT projection models replacing parallel form tables
  • Real-time event pipelines (Supabase webhooks, Postgres NOTIFY)
  • Lookup-driven business rules and feature flags
  • Webhook delivery, retries, dead-letter, and signature verification
  • Audit logs and signed-document trails
  • Reporting and admin dashboards over live data
Typical engagements

How it gets used.

  • Greenfield data platform builds
  • Real-time analytics enablement
  • SSOT consolidation across parallel intake paths
  • Data quality and observability remediation
Stack

The technologies we draw on.

We are unromantic about tooling. We pick what your team can run on a Tuesday.

PostgresSupabasePrismaDrizzleKafka-style streamsdbtResend webhooksTwilio webhooksStripe webhooks
Proof

A case study from this practice.

Freight TMS for North American Logistics — A 3PL freight forwarding TMS built on one canonical workflow from inquiry to closeout.
FREIGHT TMS FOR NORTH AMERICAN LOGISTICS · 2026otbx://
Logistics & Freight

A 3PL freight forwarding TMS built on one canonical workflow from inquiry to closeout.

An enterprise transportation management platform replacing parallel intake paths and form blobs with an SSOT-projection model. Quote wizard, shipment lifecycle, carrier rate ingestion via a local automation bot, signature workflows, Stripe payment release gating, and a chatbot-driven public quote funnel.

Read the case study
More practices

Related work.

Engagements rarely live in a single practice. These are the ones most often paired with this work.

Production-grade intelligence, engineered.

AI & LLM Infrastructure

Retrieval, agents, evaluation rigs, and multi-provider routing for AI systems you can actually ship.

Systems that survive the second year.

Software Engineering

Full-stack engineering with serious architecture: typed end-to-end, observable, accessible, and built to be owned long after we leave.

Compute as a strategic asset.

Cloud Architecture

Vercel-grade edge runtimes, Supabase landing zones, observability, and the FinOps discipline that keeps the bill defensible.

Next step

Engage the Data Engineering practice.

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