r/analyticsengineering

Every SQL course I’ve tried taught me SELECT, JOIN, GROUP BY…

Cool. But none of them taught me what to do when a PM sends:

>

…and then you discover two dashboards disagree, the data looks suspicious, and suddenly “quick one” has become your entire morning. 😅

So I built Strata.

It’s basically a fake tech company with a fake team, messy data, Slack messages, conflicting evidence, and a 90-day “first job” simulation.

You start on Day 1 as the new Analytics Engineer and have to actually work the problem instead of just answering questions someone already knows the answer to.

Before you find out whether you’re right, you have to commit to:

• your answer
• your reasoning
• how confident you are

Because getting SQL right is one thing.

Knowing when you should trust yourself is a whole different skill.

By the end, I want you to know whether you’re the “I’m 99% sure” person who is wrong 40% of the time… or the “I have no idea” person who somehow keeps getting everything right. 😂

I’ve put the first samples up for free. No email required:

https://drive.google.com/file/d/1Khv2e_dSuWMYvp-mGcgyIM6Jb1sG-3fQ/view?usp=sharing

I’m a solo builder, so there’s a very real possibility I’ve spent way too much time building something that is only interesting to me. 🙃

So before I build through Day 90, I’d genuinely love to know:

• Did you actually finish it, or did you quietly disappear halfway through?
• Did anything feel fake, forced, or too “training course-y”?
• Would you voluntarily come back tomorrow?
• And most importantly… would you want to do 89 more days of this?

Not selling anything here.

I have been using a context layer along with Claude Code for some RevOps projects (sales comp, sales360, etc.).. and I just realized that most of my context stored is not Query, but Definition and Note.

As recently I have been thinking about the differences between having a proper context layer vs storing all these information in md files in the repo, I wonder if anyone has experiences storing all these in md files? Does it actually work for your AI? Would it struggle to find the right piece of context from the sea of md files?

https://preview.redd.it/aoilj9xjcyjh1.png?width=1036&format=png&auto=webp&s=4b180011e9f704af804f36fff690689aa4127a78

We have millions of interactions and a weekly ritual where someone samples traces, adds tags and updates a ticket taxonomy. The known intents were pretty easy to graph but we had no good way of seeing the ones we didn't name yet. Recently, cancellation questions started growing alongside a frustrated sentiment cluster, while overall helpfulness and resolution scores barely even moved

I am trying to get beyond manual tagging without pretending embedding-based clustering is an oracle. Clusters need representative traces, stable labels, and enough context to distinguish a product issue from a wording change. Sampling rate matters too. A rare but expensive failure can disappear in random sampling, while one noisy customer can look like a distribution shift. Then topic drift arrives and last month’s clean taxonomy becomes another maintenance job.

Braintrust Topics is one option we are evaluating because it gives us a way to surface and track these clusters without deciding all the categories upfront. Persisting those labels for SQL queries is a big part of why we're looking at it. That sounds useful, especially if we can turn a growing cluster into an eval slice. I am still skeptical about how much human review is needed to keep labels meaningful.

I basically want an alert when a new intent or failure pattern grows materially, a handful of traces that explain it and a path to test the eventual fix. I don’t want another dashboard that says engagement changed.

How are teams detecting unknown intents and topic drift at production scale and what review loop keeps the clusters from becoming nonsense?

Hey all, hoping to get some advice from people who have dealt with this.

We use BigQuery + dbt + Looker. Looker is good for LookML + BI, but I’m starting to wonder whether it makes sense for it to be the place where all of our semantic logic lives.

We want people to be able to self-serve data through dashboards, but also through ChatGPT, internal tools, and whatever other interfaces we end up using. Ideally, someone asking the same question gets the same answer regardless of where they ask it, with the same metrics, joins, filters, definitions, and time logic behind it.

The bit I’m struggling with is that conversational analytics needs more than just a semantic model. It also needs business context, guidance on how to explore safely, known gotchas, and some way of showing where an answer came from.

At the moment, that stuff can end up spread across dbt, Looker, MCP config, prompts/skills, and whatever chat tool we are using. I don’t really want to maintain different versions of the same context for every tool.

I’ve been looking at Cube as a more headless option, where the semantic layer sits behind APIs and different tools consume it. But then I worry about what we would use for business-facing dashboards and how much extra platform work we would be taking on.

I’m also looking at Lightdash because we use dbt Core. It seems closer to what we want, but I’m still trying to work out whether it is mainly a BI tool with a semantic layer, or whether people are successfully using it as a shared layer across other clients too.

I’m not looking to rip out Looker tomorrow. I’m mostly trying to work out whether this is the right direction before going too far down the rabbit hole.

Has anyone been through something similar? What did you choose, and what would you do differently?

Hi,

Why are executives thinking, if enough context is given, AI tools like Claude will solve/build/guide everything?

In my organization, my data architecture was not build on DE fundamentals and we have accumulated a shit load of technical debt. We are told to build context (which we should any ways) and feed everything to Claude to solve it. While I agree to use for faster fixes at logic level it cannot help design or think in right way how to architect workspaces and warehouse and model the tables. It cannot be a startegist and decide the data strategy for the company. Am I thinking it the wrong way? I feel very disappointed that data professionals advice is not being heard, instead that we are being advised to put everything on Claude. Is this ok? I advice, view points.