§ Data Engineering·United States

Data engineering services in the US.

Senior-only data engineering services and solutions for US mid-market and federal teams — pipelines, data quality, and governance built so your data is actually ready for production AI. We are not a staffing shop and we don't bill bodies by the hour: every engagement is fixed-fee, fixed-scope, and handed back with a runbook. Data engineering experts who write the architecture, ship it, and leave you owning it.

Start with an AI Readiness Sprint See the engineering practice → serving clients across the United States

Most data work stalls before the AI ever ships.

The reason isn't talent or tooling — it's the foundation. Teams buy big-data platforms and headcount, then watch the AI initiative die on data nobody trusts. The fix is rarely more pipelines. It's the right pipelines, governed, observable, and pointed at a use case worth funding.

85%
of AI projects fail on poor data quality and governance (Gartner). The data foundation — not the model — is where production AI is won or lost.

So we lead with the foundation. Before we build a single pipeline, we read where your data, governance, infrastructure, and talent actually stand — then sequence the work so the first build is the one most likely to pay off. That read is the AI Readiness Sprint; the build that follows is data engineering done so it survives contact with production.

§ What we build·data engineering solutions
Data pipelines & ingestion
Batch and streaming pipelines, CDC, orchestration, and the tests that keep them honest. Built on your cloud — AWS-native by default, bring-your-cloud on request.
Data quality & observability
The checks, contracts, and lineage that turn "we think the data's fine" into evidence. Because data quality is where 85% of AI projects fail.
Data governance & access
Cataloging, access control, and policy your auditors recognize — the governance layer that lets you scale AI without scaling risk.
AI-ready data foundations
Vector stores, retrieval, feature pipelines, and the warehousing that production AI actually depends on. The bridge from data to a system that ships.
Big-data & platform work
For teams past the spreadsheet stage: distributed processing, cost-routed compute, and platform consolidation — practical big-data engineering, not a re-platform for its own sake.
Migration & modernization
Legacy warehouse and pipeline migrations with a reversibility plan and a decision log, so you can see why every call was made.
Start here
AI Readiness Sprint
A four-to-six-week fixed-scope read on whether your data is ready for AI — maturity scorecard, shadow-AI audit, and a prioritized 90-day roadmap, from $12k. The cheapest way to fund the right build first.
Scope a Readiness Sprint →
Build it
AI Engineering
Once the foundation is sound, we ship the production system on top of it — RAG, agents, pipelines, eval harness, observability, and a runbook that outlives the engagement.
See the engineering practice →
Read first
Why AI adoption is failing
The data-foundation problem, in plain terms — why most AI initiatives stall and what readiness actually looks like before you commit a build budget.
Read the framework →

A senior team, fixed-fee, and a runbook on exit.

No pyramid leverage, no rented headcount, no open-ended retainer. The people who scope your data engineering work are the people who build it — and they hand you the keys when it's done. If your data isn't ready for AI yet, that's exactly the problem we solve first.

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