Data Engineering for Professional Services

Warehouse spend runs away silently. We instrument cost per pipeline from the start, so an expensive query is visible in a day rather than a quarter.

In professional services, every hour spent on internal documentation is an hour not billed. That single fact reshapes how data engineering has to be built here, the guardrails, the approval points and the evidence trail are design inputs rather than things bolted on before go-live.

The workload we are most often asked to take on first is proposal and pitch drafting, usually integrated against practice management. We build the smallest thing that proves the case, put it in front of real users, and expand only what earns its keep.

Deployed across regulated and unregulated sectors, with audit trails where the regulator expects them. We hand over with runbooks, tests and a team that knows how it works, not a dependency.