My experience is that monitoring data quality is a still an under-appreciated discipline. I've found that most teams still have an "not invented here" mentality, or don't even know they have the problem! That can lead to a "oh, we can just fix it when it happens" type of mentality. But your timing may be better than ours - we started back in 2016.
I haven't played with your product (yet), only took a look at this thread and your website. Some observations:
- SQL Editor - big plus! I think giving your users a space where they can take action is a super value-add, we didn't have that.
- nice work running the tests inside the customer's warehouse. That has two benefits for you. 1) you're not incurring the cost to crunch the metadata, it can get quite expensive, depending on the number of tables in the warehouse. 2) you're avoiding data access issues, getting access to the warehouse was always a hurdle, even though we only needed access to the system tables.
- pricing model. I think the per-seat model is the way to go. We tried charging by number of rows, and size of the warehouse (number of nodes), but then you run into weird situations with customers who are dealing with huge historic datasets, but really only look at the last 30 of data.
My unsolicited $0.02 is that you think hard about distribution. I think you want to think about hitching your wagon to the cloud marketplaces, and Snowflake's marketplace. For example, attaching themselves to Snowflake is what made all the difference for Fivetran.
I have a bunch of more scars that I can share if you care to know them :-)