We’re working on automation around how we manage dbt schemas and tests – particularly maintaining dbt models (schemas and tests) for our ever-changing raw analytics events, which are typically updated with every feature release. Our trigger for kicking this off was that we just finished an internal hackathon where we ended up building a dbt utility that we’re excited to take further and open source.

Right now we’re particularly focusing on metrics that we compute by tying together those ever-changing raw analytics events (e.g. "Account Created", "App Opened", "Code Generated") with data from our backend and 3rd party tools like Stripe.

We want to make sure to build something that makes sense for a wider and doesn’t reinvent the wheel – so we’d love taking into account some HN perspectives.

If you’re up for contributing to our research (and maybe even building with us), we’d appreciate your answers to this quick 4-9 min survey. It’s a mix of situational questions (your data stack) and open questions on how you manage the steps from when a success metric has been defined (pre feature release) until the insights are visualized.