Scaling a data consultancy with Skippr
Industry: Professional services · Size: 20 employees · Clients: Mid-market · Previous model: Custom ETL per client
A boutique data consultancy used Skipprd as the delivery substrate — tripling concurrent client capacity.
| Before | After | |
|---|---|---|
| Delivery | 3–6 months | ~2 weeks |
| Concurrent projects | 4 | 12 (same headcount) |
| Per-project margin | ~35% | ~60% |
Problem
Every engagement was bespoke Python, Airflow, and Glue. Two to three consultants per client capped the firm at four concurrent jobs. Larger firms won on timeline and brand. Building an internal platform was estimated at 18 months.
Solution
The firm standardised on Skippr: deploy into the client environment, connect sources, review discovered schemas, land the warehouse, then spend week two on models and dashboards. Schema discovery replaced weeks of mapping interviews. Consultants completed Skippr training and joined the partner program.
Typical first days:
sde init client-acme
sde connect warehouse bigquery --project acme-analytics
sde connect source mssql --connection-string '${ACME_DB}'
skipprd discover
skipprd sync --once
sde modelOutcome
- Delivery 3–6 months → about 2 weeks.
- Concurrent projects 4 → 12 with the same team.
- Recurring subscription management beside implementation fees.
See ELT for consultancies. More stories: Customers.