AI for your role

AI for Analytics Engineers

Let AI scaffold the models so you can obsess over the tests that make data trustworthy.

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The shift

How AI is changing the Analytics Engineer role

In 2026, AI copilots scaffold dbt models, write SQL transformations, and draft tests and documentation faster than any Analytics Engineer could by hand. That makes the role more valuable, not less: value concentrates in modeling data so downstream teams can trust it, designing tests that catch silent breakage, and shaping the semantic layer the whole company reasons on.

What AI can take off your plate

  • Writing dbt models and SQL transformations from a spec
  • Generating schema tests and data-quality assertions
  • Drafting model documentation and lineage descriptions
  • Refactoring repetitive transformation logic
  • Explaining a failing test or a data-quality incident

What stays distinctly human

  • Modeling data so downstream trust is earned, not assumed
  • Designing tests that catch silent data breakage
  • Owning metric definitions in the semantic layer
  • Making grain and dimensional-model decisions
  • Balancing warehouse cost, freshness, and performance
Tools

Five AI tools for Analytics Engineers

dbt (with dbt Copilot)
Generates models, tests, and documentation from natural language and centralizes metrics in the semantic layer.
Try it →
ChatGPT
Writes and refactors SQL, drafts dbt tests, and explains a gnarly window function or a failing assertion.
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Claude
Reviews a transformation for grain and join bugs, proposes edge-case tests, and drafts clear model docs from your code.
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Cursor
An AI-native editor that understands your dbt project and helps write, refactor, and debug models across files.
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Datafold
AI-assisted data-diffing and column-level lineage that shows exactly what a change to a model will break downstream.
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Prompts

Five prompts to try today

Paste these into Claude or ChatGPT and replace the bracketed parts with your own details.

1. Draft a dbt model
I need a dbt model for [purpose] from source tables [describe]. Write the SQL following [conventions], set the right grain, and suggest the schema tests I should add.
2. Design the tests
Here's a dbt model: [paste]. What silent failures could slip through — null spikes, grain changes, broken joins, duplicates? Write dbt tests to catch each.
3. Debug a data-quality incident
A downstream metric jumped [describe]. Here are the relevant models [paste]. Walk through the likely causes upstream and how to isolate which transformation introduced it.
4. Optimize warehouse cost
This model is expensive [paste SQL and describe run frequency]. Suggest incremental strategies, materialization changes, or refactors to cut cost without hurting freshness.
5. Define a semantic metric
We want a governed definition of [metric] in the semantic layer. Propose the definition, the dimensions it should be sliceable by, and the tests that keep it honest.
The playbook

Every AI play for Analytics Engineers

Your full AI playbook for your role — updated every week. Tap any card for a step-by-step walkthrough and examples.

✦  New AI plays are added every week — and go straight to subscribers in their morning brief. Skip the scrolling and get yours delivered free. Get my free brief →
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A day in your inbox

This is the kind of brief a Analytics Engineer gets, every weekday morning.
Monday morning
✦ Personalized for: Analytics Engineer
Data PlaybookWriting and debugging SQL
Fix the query that returns nothing

A query runs clean but returns zero rows. The bug is in your join or filter, not your syntax.

Claude  FREE  reads your SQL and spots the logic error

The old way
You re-read the same 40 lines six times and start commenting out WHERE clauses at random.
The AI way
You paste the query, the schema, and what you expected. You get the likely cause in one read.
This [Postgres/MySQL/BigQuery] query returns 0 rows but should return data. Schema: [paste CREATE TABLE or column list]. Here is the query: [paste SQL]. I expected [what you expected]. Find the bug. Check join type, filter order, NULL handling, and date ranges. Explain what is wrong and give the fixed query.

Why it works: Most zero-row bugs are an inner join that should be left, or a filter that drops NULLs. A second reader catches those fast. You keep control of the fix.

Your role, all in one place
  
Tools, prompts & tricks
Your full library, one tap away.
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Your playbook
Every entry, building each week.
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How AI is changing your role
Where your work is heading.
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