Data engineering that ends the spreadsheet month-end.
Pipelines from every source, a warehouse with tested models, and dashboards people trust, so finance, product and your AI features all read the same numbers. Published build: seven sources, 34 dbt models, reporting cut from 56 hours a month to 4.
What data engineering services should fix first.
Most companies do not have a data problem. They have seven systems that disagree. Gigabit connects them into one warehouse, unifies customer IDs, models the metrics in tested code and puts self-serve dashboards on top. The same warehouse then feeds forecasting and AI features, which fail fast on messy data.
In the published engagement a $6M ARR SaaS company connected HubSpot, Stripe, QuickBooks, product usage and three other sources into Snowflake with Fivetran, then got 34 dbt models and six Metabase dashboards. The build cost $44,000 over seven weeks. Manual reporting fell from 56 hours a month to 4, month-end close from five days to two, and answering a business question from hours to about 30 seconds.
What we build.
From the first connector to the dashboard your board reads, with tests at every layer.
Pipelines and ingestion
Managed connectors where they exist, custom pipelines where they do not, with sync schedules matched to how fresh each source must be.
Warehouse architecture
Snowflake, BigQuery or Postgres with raw, staging and business-ready layers, so every number traces back to its source.
Metric models in dbt
MRR, ARR, churn, cohorts and health scores as tested code, so corrupt source data stops the pipeline instead of reaching the board deck.
Dashboards and self-serve BI
Metabase, Looker Studio or Power BI dashboards for finance, product, support and leadership, with training for each team.
Data for AI
Clean, documented tables and retrieval pipelines that agents and AI features can rely on.
Access and governance
Roles, row-level access and audit trails, including HIPAA-aware set-ups for healthcare data.
Seven weeks, in the published build.
Assess
Audit every source and rank the reports that cost the most hours.
Ingest
Connect the sources into a layered warehouse with unified IDs.
Model
Build the metrics in dbt, each guarded by data quality tests.
Serve
Ship the dashboards and train the teams who use them.
Published prices, mapped to the job.
Every price here is the same one on the pricing page. Work with no published price gets a fixed price in writing before it starts.
Figures in the notes are what past clients paid, from the published case studies; they show the range, not a rate card. Work with no natural end, such as ongoing development, is priced per engineer per month with no lock-in.
When to hire us for this, and when not to.
- Month-end reporting takes days of exports and formulas.
- Revenue or customer numbers differ depending on which system you ask.
- You plan AI features or forecasting on your own data.
- Investors or the board ask for metrics you cannot produce quickly.
- One system holds everything and its built-in reports answer your questions.
- You want a dashboard without fixing the data underneath. It will be wrong within a month.
Two data builds, with the results.
A SaaS analytics stack on Snowflake and dbt, and a healthcare warehouse on BigQuery behind 14 locations.
A 7-week modern data stack eliminated 56 hours/month of manual reporting, cut month-end close from 5 days to 2, and preserved $186K in ARR.
Unified HIPAA-aware intake with EHR integration cut intake/admin time 41% and lifted clinician throughput 16% across 14 locations.
These are past engagements at the prices those clients paid. For build price, monthly cost and payback side by side, see what ten client projects cost.
Data engineering, answered
How much do data engineering services cost?
Our published build cost $44,000 over seven weeks, after a $4,000 one-week assessment, for seven sources, 34 dbt models and six dashboards. Year-one total cost including tooling and support was $78,200. Smaller scopes cost less; you get a fixed price after the assessment.
How long does a data warehouse take to build?
Six to ten weeks for most first builds. The published SaaS build took seven weeks from assessment to trained teams.
Which tools do you use?
Snowflake, BigQuery or Postgres for the warehouse, Fivetran or custom pipelines for ingestion, dbt for modelling, and Metabase, Looker Studio or Power BI for dashboards. We pick based on your data volume, budget and team.
Do you offer data warehouse consulting without a full build?
Yes. The assessment works on its own: a written review of your sources, the reports that cost the most time and a plan with a price. Your team can build from it, or we can.
Is our data ready for AI?
The assessment answers that. AI features need clean IDs, consistent definitions and documented tables; the same work that fixes reporting makes AI reliable. The free AI Readiness Assessment is a quick first check.
Often scoped alongside.
From the field notes
Tired of reconciling exports?
List the systems and the reports that eat the most time. We reply with what the assessment would cover and what a build would cost.