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.
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.
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 senior bench 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 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.
It matters who does the work. A junior bench learns your domain on your budget and leaves the hard architectural calls undocumented; a senior-only team makes those calls deliberately, writes down why, and builds the foundation to survive contact with production rather than the next demo. That is the difference between a data engineering partner and a staffing invoice — and it's why 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.