Data engineering services and solutions for US mid-market and federal teams — pipelines, data quality, and governance built so your data is ready for production AI, implementation work, and private AI programs. 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.
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.
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.
Most clients arrive with a named problem, not a generic "data" mandate. These are the service-intent engagements we run most often — each scoped to a fixed fee and handed back with a runbook.
Our data engineering consulting fits mid-market companies and funded startups that have outgrown spreadsheets and one-off scripts but don't have a standing platform team. You have data in three systems that don't agree, an AI initiative waiting on a foundation that isn't there, and an analyst spending half their week firefighting pipelines instead of answering questions. The fix is rarely more headcount — it's the right pipelines, governed and observable, pointed at a use case worth funding.
We are also a deliberate fit for federal and regulated buyers who need a defensible data foundation under an audit clock. The same governance, lineage, and access discipline that satisfies an auditor is what lets you scale AI without scaling risk — so the foundation work pays off twice.
Every data engineering engagement is scoped to a named outcome and a fixed fee. The baseline build covers the work that turns scattered, untrusted data into a foundation production AI can stand on.
We are not a staffing shop and we do not bill bodies by the hour. Data engineering runs as fixed-fee, fixed-scope sprints, typically four to twelve weeks, with a runbook on exit. Most engagements start with a readiness read so the first build is the one most likely to pay off — not sure where you stand yet? Start with our free data engineering assessment, a fast self-serve read on your data foundation before you scope a sprint.
A fixed-scope read on whether your data is ready for AI — maturity scorecard, shadow-AI audit, and a prioritized 90-day roadmap. The cheapest way to fund the right build first.
Pipelines, quality, governance, and the warehouse or lakehouse layer — scoped to a fixed fee off the readiness read so you approve a number, not an open-ended retainer.
Legacy warehouse and pipeline migrations with a reversibility plan and a decision log — moved without a big-bang cutover, handed back for your team to run.
How a sound data and retrieval foundation stops the million-dollar chunking mistake — the engineering that decides whether AI ever ships.
Read the framework →How enterprise AI deployment actually gets architected from scratch — the data plumbing underneath every system that reaches production.
Read the framework →An edge-to-cloud framework for manufacturing — the streaming pipelines and feature stores that carry sensor data to a live model.
Read the framework →AWS-native by default — S3, Glue, Lambda, Step Functions, and the warehouse or lake that fits the workload — with bring-your-cloud on request. Where a team has standardized on the Lakehouse, we run pipelines and governance on Databricks: Unity Catalog for cataloging and access, Delta for reliable tables, and the orchestration that keeps it honest. The tooling serves the outcome; we don't re-platform a team that's already productive.
Engagements are fixed-fee and scoped up front. Most start with an AI Readiness Sprint from $12k (four to six weeks), which sizes the foundation work before you commit to it; the data build that follows is scoped to a fixed fee off that read, typically a four-to-twelve-week sprint. You approve a number, not an hourly meter.
Almost never. The fix is rarely more pipelines — it's the right pipelines, governed and observable, pointed at a use case worth funding. We don't re-platform a team that's already productive; we close the specific gaps the readiness read finds.
AWS-native by default, bring-your-cloud on request. Where a team has standardized on the Lakehouse we run on Databricks with Unity Catalog and Delta. The tooling serves the outcome, not the other way around.
A hire owns one seat; a fixed-fee engagement delivers a foundation — pipelines, quality contracts, governance, and a runbook — built by a focused delivery team and handed back so your team can run it without depending on us.
Yes. Migrations ship with a reversibility plan and a decision log, moved in stages rather than a big-bang cutover, so you can see why every call was made and roll back if needed.
That is the point. 85% of AI projects fail on data quality and governance, so we fix the foundation first, then route the AI build — into our machine learning practice or your own team.
Both. DataOps implementation services bring CI/CD, testing, and observability to your pipelines so changes ship safely; MLOps consulting extends the same discipline to models — feature pipelines, a registry, drift monitoring, and rollback. We stand up the practice and hand it back with a runbook, rather than leaving you a set of scripts.
Yes — data lakehouse consulting is a core engagement. We build lakehouse architecture on Delta or Iceberg with a unified catalog so analytics and ML share one governed source of truth, on Databricks with Unity Catalog or open-table on your cloud. For broader platform rethinks, our modern data architecture work covers medallion layering, streaming-first design, and cost-routed compute — written down so every decision is defensible.
No pyramid leverage, no rented headcount, no open-ended retainer. The people who scope your data engineering work stay accountable for the architecture and handoff. If your data is not ready for AI yet, that is exactly the problem we solve first.
It matters who does the work. A weak data foundation turns governance, implementation, and private AI into theater. We make the architectural calls deliberately, write down why, and build the foundation to survive contact with production rather than the next demo. The runbook we leave behind is a system your team can actually operate, not a black box that breaks the first time the data shifts.