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 Skippr Cloud ELT 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:

skippr init client-acme
skippr connect warehouse bigquery --project acme-analytics
skippr connect source mssql --connection-string '${ACME_DB}'
skippr discover
skippr sync --once
skippr model

Outcome

• 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.