Service · Data Engineering
Data warehouse consulting that ships.
Semantic model first, platform chosen last, one subject area live in production within weeks, and a build-out priced by milestone so you can stop at any boundary. Designed for mid-market estates and the regulated industries where the warehouse has to stand up in an audit.
What does a data warehouse consultant actually do?
A data warehouse consultant designs the model your business data lives in, builds the pipelines that load it, puts governance around it, and hands the result to your team in a state they can run without the consultant. The model is the part that matters: it is the shared definition of what your entities mean, and everything downstream, every dashboard, every report, every AI feature, inherits it.
We do this as senior engineers who stay through delivery, for companies where the data carries compliance weight: pharma, financial services, and the private equity portfolio companies whose warehouses have to stand up in diligence. The same discipline underneath our data engineering practice applies here; this page covers the warehouse-shaped version of it.
The method
How should you design a data warehouse?
Design the warehouse in this order: the business questions, the semantic model, the pipelines, and the platform last. Most failed warehouse projects ran this list backwards; this is the right order, run as an engagement.
1
Start from the questions, not the platform
The first artifact is a list of the questions the business needs answered, ranked by who is asking and how often. The warehouse exists to answer them. A design that starts from a vendor diagram instead of this list produces a warehouse that is technically correct and commercially useless.
2
Model before you move data
The semantic model, the shared definition of what a customer, an order, or an exposure actually means, comes before any pipeline. We work in dimensional modeling terms (Kimball's framing, because your analysts already think in facts and dimensions) with a medallion-style layering for raw, cleaned, and consumable data. Get this layer right and every dashboard, report, and AI feature on top gets cheap. Get it wrong and every consumer builds its own contradictory version.
3
Choose the warehouse last
Snowflake, BigQuery, Redshift, Databricks, or Postgres-based: by the time the model and the workload are understood, the platform choice is usually obvious and rarely the interesting decision. We are vendor-neutral. The expensive mistake is choosing first and modeling around the choice.
4
Prove it on one subject area
The first slice is one subject area, source to dashboard, in production with tests and lineage. Weeks, not quarters. It validates the model, the pipeline pattern, and the governance approach while the surface area is still small enough to change cheaply. The rest of the build repeats a proven pattern instead of a hopeful one.
What the model buys you
Before · "what was revenue last month?"
Four tools, four answers. Each computes "revenue" its own way, and the Monday meeting starts with an argument about whose number is right.
The model · defined once
Net revenue means gross sales minus refunds, counted when the goods ship.
Written into the warehouse itself, not into each tool, so nothing downstream can quietly disagree.
After · every surface, same answer
One definition, inherited everywhere, including by the AI. The argument is over before the meeting starts.
The regulated version
What changes when the warehouse is regulated?
In a regulated business the warehouse is part of the compliance surface: it feeds regulatory reports, evidences decisions, and holds data with retention obligations. Four things get built differently from day one. The wider context lives on our regulated-industry page.
Lineage as evidence, not decoration
When a regulator or auditor asks where a number came from, the answer has to be a traceable path from source to report, not an engineer's recollection. We build column-level lineage in from the first pipeline.
Access control that maps to your compliance story
Role-based access on the consumable layer, row and column policies where the data demands it, and an access log an auditor can actually read.
Retention and deletion that actually execute
Policies that exist in a document but not in the pipeline are findings waiting to happen. Retention windows and deletion obligations get implemented as code, with proof they ran.
Change control without paralysis
Schema changes reviewed, versioned, and reversible, so the warehouse can evolve at engineering speed while still producing the audit trail regulated environments require.
How the engagement runs
Three phases, each with a stop-point
The same milestone discipline we apply to modernization work: fixed-price phases, acceptance criteria, and the right to stop at every boundary.
Phase 1 · 1-2 weeks · fixed price
Design read
The question inventory, source audit, semantic model draft, platform recommendation, and a milestone-priced plan for the build. You keep every artifact whoever does the building.
Phase 2 · Typically 3-6 weeks
First subject area live
One subject area in production: sources connected, model implemented, tests and lineage in place, first consumers migrated. The gate is real consumers doing real work on it.
Phase 3 · Priced per phase
Build-out by milestone
Remaining subject areas in dependency order, each a fixed-price milestone with acceptance criteria. You can stop, redirect, or extend at every boundary.
Receipts
Data platforms we have actually shipped
Analysis time: ~2 years → ~3 months
The far end of the scale, not a first slice: a three-layer research platform over hundreds of thousands of channels, grown subject area by subject area by a 14-specialist team across 18 months, ending in 87% less engineering effort per analysis.
Read the case study MMIT · Pharma dataAutonomous pharma data extraction
Healthcare data operations in a compliance-heavy domain: extraction pipelines that run without babysitting, feeding analytics the business actually trusts.
Read the case studyFrequently asked
Questions we get
What is data warehousing consulting?
Data warehousing consulting is bringing in outside engineers to design, build, or fix the central analytical database a business runs its reporting and analytics on. In practice the work is four things: modeling the data so it means the same thing everywhere, building the pipelines that load it, putting governance around it, and handing the result to your team in a maintainable state. The valuable part is the modeling; the pipelines are increasingly commodity.
What are the top 5 data warehouses?
By adoption: Snowflake, Google BigQuery, Amazon Redshift, Databricks SQL, and Azure Synapse, with Postgres itself, DuckDB's embedded OLAP engine, and ClickHouse's column store increasingly credible at the mid-market scale where warehouse licensing starts to bite. The honest answer is that for most mid-market workloads any of the top five works, and the differentiator is the model and the pipelines you put on it, which is why we choose the platform last.
What is the difference between a data lake and a data warehouse?
A data lake stores raw data in open formats cheaply and figures out structure at read time; a data warehouse stores modeled, governed data optimized for answering business questions fast. Most real estates now run a layered hybrid: lake-style raw storage feeding a warehouse-style consumable layer. If someone tries to sell you one as a replacement for the other, they are selling their product category, not your architecture.
How much does data warehouse consulting cost?
Our design read is fixed price and agreed before we start; the build phases are each fixed-price milestones scoped by the read, so the total is a function of how many subject areas you actually need rather than a day rate multiplied by hope. The pattern to avoid is the open-ended time-and-materials build, which is how warehouse projects become multi-year line items.
Can you fix an existing warehouse rather than build a new one?
Yes, and it is often the right call. A warehouse with a broken model gets remodeled in place, subject area by subject area, the same strangler-fig way we approach any legacy modernization. A warehouse that is modeled well but slow or expensive is usually a platform and pipeline problem, which is a cheaper fix than people fear. The design read tells you which case you have. When the problem is wider than the warehouse, the whole-estate version is data platform modernization.
Why does the warehouse matter for AI projects?
Because the model layer is what AI features actually consume. Retrieval, agents, and analytics all inherit whatever definitions and quality the warehouse gives them. We covered the argument in why your AI project is actually a data project: the teams that trust their data first get to ship AI cheaply; the teams that bolt AI onto an unmodeled estate ship demos.
Start with the design read.
One to two weeks, fixed price agreed before we start. You get the question inventory, the semantic model draft, the platform recommendation, and a milestone-priced build plan you can execute with us or without us.