Data Engineering for Teams Drowning in Spreadsheet Reports

Most companies do not have a reporting problem. They have a data problem. The Stripe export does not reconcile with QuickBooks. The HubSpot pipeline lives in a different shape than the product database. Someone built an overnight Python script two years ago, that person left, and now nobody is sure whether last quarter's revenue number is right. Every executive meeting starts with ten minutes of arguing about whose spreadsheet is correct.

Data engineering fixes the layer underneath the reporting. We pull every source into one warehouse, model it into clean tables that mean the same thing everywhere, and put automated tests around the numbers so a broken source fails loudly instead of silently corrupting a board deck. Once the data layer is trustworthy, the dashboards and analytics built on top of it finally agree with each other.

One-week data audit first. We map your sources, the questions you are actually trying to answer, and where the current numbers diverge. You get a one-page document with the proposed warehouse, the source list, and a phased estimate. The audit is billed separately at $2,500 so you can decide before committing to the full build — and it is useful even if you hand it to another team.

From there we build in vertical slices: one source, modeled end-to-end into a tested mart, with a working query on top, before adding the next. You see correct numbers in week two instead of waiting two months for a big-bang launch. We hand off the warehouse, the dbt project, and the runbooks to your team — no proprietary platform to rent.

We treat the warehouse as a product, not a dumping ground. Raw sources land untouched in a staging schema so you always have an audit trail back to the original system. Transformations are SQL in version control, reviewed like any other code, with tests that gate every change. The result is a warehouse where the lineage of any number — from board metric back to source row — is one query away.

We dogfood this. Our own sales and revenue reporting runs on the same ELT-into-Postgres-with-dbt pattern we ship to clients: deduplicated contacts, reconciled Stripe and QuickBooks figures, and tested marts behind every dashboard. We build the data layer the way we would want to inherit it.

Founder-led from audit to handoff, delivered remotely to clients across the United States from our base in Macon, Georgia.

Fixed-fee per scope. Typical ranges:

• One-week data audit with warehouse recommendation: $2,500 flat
• Postgres warehouse with two to three sources and a tested dbt model: $12k – $28k
• Multi-source warehouse with incremental loads and a semantic layer: $30k – $60k
• BigQuery or Snowflake platform with change-data-capture and many sources: $55k – $90k
• Spreadsheet-and-script teardown rebuilt as a tested, orchestrated pipeline: $18k – $45k

Optional monthly retainer for new sources, model changes, and pipeline maintenance as your reporting needs grow.