Sigma vs hiring a data engineer
Your data project shouldn’t wait on a hire.
A first data engineer takes months to recruit and ramp, then spends much of their time keeping pipelines alive. Sigma does that engineering from day one.
Backed by Y Combinator
Your first 90 days
Two ways to start the same project.
Hiring a data engineer
Months before the first report
Months 1–3
Write the role and recruit
Months 3–4
Onboard and learn your systems
Months 4–6
Build the first pipelines
After that
First report, then upkeep
Sigma
Starts right away
Step 1
Connect your systems
Step 2
Sigma maps and cleans them
Step 3
First answers, with sources
Step 4
Workflows running
What the job actually is
44 steps. That’s the job description.
Before a warehouse can answer a question in plain English, someone has to transform the data, model it and build a semantic layer, by hand, in SQL. Then keep it all working.
15
to transform the data
14
to model it
15
for the semantic layer
The short answer
Start the project now. Hire when you’re ready.
A data engineer is a strong hire once you have the volume, but the first one usually means a six-figure salary plus months of recruiting and ramp-up before the first useful output, and much of their time then goes to maintenance. Sigma automates the data engineering, from connecting systems and matching records to modeling and keeping it all working, so you can start on the project now. Any data hire you make later builds on top of it instead of from scratch.
When to hire anyway
Sometimes a hire is the right call.
FAQ
Questions, answered.
Do I need a data engineering team to use Sigma?
No. Sigma handles the engineering behind your data workflows, so you can focus on what you want to accomplish.
Can my existing data people use Sigma?
Yes. It takes the plumbing off their plate, so they can spend their time on the questions only they can answer.
