dbt Content Sequence — Robert GPT
Campaign: dbt audit & analytics engineering
Format: Robert GPT (Format Index + linkedin-patterns.md)
Source evidence: Demilade–Luke dbt audit service call (2026-01-20), Uttam consulting patterns, Urban Stems / Magic Spoon case details
Last updated: 2026-02-04
| # | Pillar | Topic / hook | Format used |
|---|---|---|---|
| 1 | Problem | Hidden cost of dbt debt during onboarding | Diagnostic List |
| 2 | Problem | Why new analytics engineers take 3 months to ramp | Diagnostic List |
| 3 | Solution | How top teams document dbt for knowledge transfer | Silo-to-Signal / teaching |
| 4 | Solution | dbt testing strategy that prevents issues | Problem → Common Fix → Better Fix |
| 5 | Service | What we audit in a dbt code review | B2B Framework (list) |
| 6 | Service | dbt audit → roadmap (case study) | Process Reveal + case |
Content creation sequence
Use this workflow when building campaign content (not just dbt). Each step gates the next.
1. Structure selection
- Check Robert GPT or Uttam GPT (per campaign/brief).
- In that GPT: open LinkedIn Format Index and memory/examples/linkedin/ (and README if present).
- Decide post type (diagnostic, problem-solution, framework, event, partnership, etc.) and pick the format (and structure pattern) that matches.
- Output: chosen format name + pattern (e.g. Diagnostic List, Problem → Common Fix → Better Fix).
2. Draft
- Use knowledge files the user references (e.g. transcripts, playbooks, case notes).
- Use key resources the user asks you to check (e.g. specific transcripts, positioning docs).
- Draft in the chosen structure; output in Campaign Post Template shape (First Draft: Post + Carousel; Outline: Facts, Implications, CTA).
3. Alignment check
- Check against positioning (Brainforge POV, how we talk about ourselves).
- Check against sales (how this service is sold, what the offer is, who buys it).
- Check against meeting/context (how this service line fits into the bigger story—e.g. WBR priorities, partner motions, other service lines).
- Adjust tone, claims, and framing so the post fits the greater context.
4. CTA evaluation
- Use the CTA framework (see below) to choose the CTA for this post.
- Do not default to “DM us” variations. Pick based on post goal, funnel stage, and what we’re testing.
Possible gaps (confirm or add):
- Audience/ICP: Explicitly confirm the post speaks to the right buyer (e.g. analytics engineer, data lead, head of data).
- Campaign/brief: Confirm the post supports the campaign goal and key message from the brief.
- Review: Who reviews before publish (e.g. Robert for sales-related, Hannah for engagement path)?
- Learnings loop: After the post runs, add wins to the GPT’s examples or pattern confidence so the system improves (already in Campaign Post Template).
CTA framework (reference)
Full framework: CTA_FRAMEWORK.md
Summary for this sequence:
- 5 engagement tiers (increasing intent): 1) Tracked links clicked, 2) Lead magnet downloads, 3) Event signups, 4) Direct messages, 5) Meetings booked from actions. We measure all five; don’t only optimize for DMs.
- Content → CTA: Problem posts → tracked link or DM (tiers 1, 4). Solution posts → lead magnet (tier 2). Service/trust posts → event or meeting (tiers 3, 5).
- 2-week loop: Run a mix of CTAs, fill the tracking table by tier, read the signal, adjust. Target: 25 total high-intent engagement per period (see CTA_FRAMEWORK.md).
When choosing a CTA for each dbt post, pick the tier you want to capture and use a CTA that matches. For non-DM options (tiers 1, 2, 3, 5), see “What to set up” in CTA_FRAMEWORK.md and the suggested CTAs + setup per post below.
CTA experiments (use these to test)
Goal: Vary CTAs across the sequence so we capture different engagement tiers (see CTA_FRAMEWORK.md). Don’t default to “DM us” for every post. Below, each post has suggested non-DM CTAs (tiers 1, 2, 3, 5) with what to set up so we can measure. When you pick a CTA, note the tier and record results in the tracking table.
Tier mapping:
- Tier 1 — Tracked links clicked: Link in post (e.g. to one-pager, blog). Set up: UTM or short link per post so clicks are countable.
- Tier 2 — Lead magnet downloads: Comment keyword or gated form. Set up: Asset + form or manual log when you send via DM.
- Tier 3 — Event signups: Event/webinar link. Set up: Event page with registration; count signups.
- Tier 4 — Direct messages: “DM us…” No extra setup; log DMs weekly.
- Tier 5 — Meetings booked from actions: Booking link. Set up: Calendly/Cal.com link (or campaign-specific link) + “How did you hear about us?” so we can attribute.
Post 1 — Hidden cost of dbt debt during onboarding
Pillar: Problem
Topic: Hidden cost of dbt debt during onboarding
First Draft
Post
The real cost of dbt debt doesn’t show up until someone new joins.
The person who built it has learned to work around the broken test that’s been failing for years. They know which models not to touch. They know that “dbt is still running” is the answer to half the questions. To them it’s background noise. To a new hire it’s a minefield.
Here’s what actually happens when you onboard into a messy dbt codebase:
-
Tests have been failing so long everyone ignores them. The team says “that’s just how it is.” The new person doesn’t know which failures are real and which are legacy noise. They either waste time fixing non-issues or learn to ignore tests too, and the next real issue slips through.
-
Nothing is documented. The logic lives in someone’s head, and often that person has left. The new analytics engineer is reading 600 or 800 lines of code to figure out what a model does and why it was built that way. There’s no “what” or “why,” only “what’s there.”
-
One person was doing everything. We’ve seen it over and over: one data person building what the business needs every day. They don’t have the bandwidth to stop, audit, and refactor. So the mess compounds. When you finally hire help, the new person inherits a system that was never designed for two people to understand.
Add it all up, and onboarding becomes a multi-month archaeology project instead of a productive ramp. The hidden cost isn’t the salary. It’s the months where both people are stuck in “how does this even work” instead of “how do we make it better.”
This isn’t a talent problem. It’s an infrastructure debt problem that shows up at the worst time.
If this sounds like the codebase you’re handing to your next hire, you’re not alone. We’ve audited stacks where the only way to understand the system was to trace it line by line. DM us if you want to talk through what an audit would surface before the new person starts.
Carousel
CAROUSEL 1 — Hidden cost of dbt debt during onboarding
Slide 1 (Hook)
The cost of dbt debt shows up when someone new joins.
Slide 2 (Reality)
To the builder it's background noise. To the new hire it's a minefield.
Slide 3 (What breaks)
• Tests failing for years → everyone ignores them
• No documentation → 600–800 lines to decode
• One person did everything → no design for two people
Slide 4 (Consequence)
New hire can't tell real failures from legacy noise.
Slide 5 (Business consequence)
Onboarding = multi-month archaeology, not productive ramp.
Slide 6 (Reframe)
Not a talent problem. An infrastructure debt problem.
Slide 7 (CTA)
Audit before the new person starts. DM us.Outline
POST 1 — Hidden cost of dbt debt during onboarding
FACTS / EVIDENCE:
- Teams learn to ignore tests that have been failing for years; new hires don’t know which failures matter
- Without documentation, new people must read hundreds of lines of code to understand logic and intent
- Solo data owners build under “we need things to happen” pressure and rarely have bandwidth to audit or refactor
- When a second person joins, the system was never designed for two people to understand
IMPLICATIONS:
- Onboarding stretches into a long archaeology project instead of a fast ramp
- Both incumbent and new hire spend time on “how does this work” instead of improving the system
CTA (current): DM us to talk through what an audit would surface before the new hire starts — Tier 4
Non-DM CTAs (suggested):
| Tier | CTA | What to set up |
|---|---|---|
| 1 | ”See what we look for when we audit for onboarding risk: [tracked link].” (Link to a short blog or one-pager.) | Create a tracked link (UTM or Bitly) for this post; log clicks. |
| 2 | ”We put together a checklist: what we actually look for when we audit for onboarding risk. Comment CHECKLIST and I’ll send it.” Or: “Get the onboarding-risk checklist: [link to gated form].” | Asset: 1-pager or checklist. Delivery: manual (DM when they comment CHECKLIST) or form that emails asset; log downloads. |
| 5 | ”Book a 30-min audit preview so you know what we’d surface before your next hire starts: [booking link].” | Dedicated Calendly/Cal.com link for “dbt audit preview” (or campaign); add “How did you hear about us?” to attribute. |
Post 2 — Why new analytics engineers take 3 months to ramp
Pillar: Problem
Topic: Why new analytics engineers take 3 months to ramp
First Draft
Post
New analytics engineers don’t take 3 months to ramp because they’re slow. They take 3 months because the codebase was never built for anyone else to read.
We’ve seen it repeatedly. You hire someone sharp. They can write SQL, they understand the business. But they land in a dbt project where models are 600-line monoliths, there’s no staging → intermediate → mart clarity, and the only documentation is “we’ll fix that later.” Every question leads to “go read the code” or “ask so-and-so.” So-and-so is the only one who knows why that test has been red for two years and why it’s “fine.”
Here’s what actually stretches the ramp:
-
No modularity. When something breaks, the new person is debugging “line 585” in a file that does ten things. There’s no way to isolate the problem. In a modular setup you trace to a 100-line slice and fix it. In a monolith you’re guessing.
-
No clear DAG. Staging feeds intermediate feeds mart is the idea. We’ve seen mart models feeding back into intermediate. Cycles in the graph. The new person has to reverse-engineer the flow before they can safely change anything.
-
Assumptions live in people’s heads. What’s the grain of this table? Why do we exclude those rows? The logic isn’t written down. So every change is a risk. The new engineer either blocks on the incumbent for every decision or makes a change and breaks something nobody knew depended on it.
The result is predictable. Months of “where does this come from?” and “why was it built this way?” before they can own a single improvement. It’s not a capability gap. It’s a design gap. The system was built to ship, not to transfer knowledge.
This isn’t really a hiring problem. It’s a “we never made the codebase readable” problem.
If your team is about to bring on a second data or analytics engineer and the first one is the only one who can navigate the dbt project, it’s worth asking what an audit would find. We do audits that map the real structure, document assumptions, and produce a roadmap so the new person has something to lean on. DM us if you want to see what that looks like.
Carousel
CAROUSEL 2 — Why new analytics engineers take 3 months to ramp
Slide 1 (Hook)
They're not slow. The codebase wasn't built for anyone else to read.
Slide 2 (Reality)
600-line monoliths, no DAG clarity, "ask so-and-so" for every why.
Slide 3 (What breaks)
• No modularity → debug line 585 in a 600-line file
• No clear DAG → cycles, reverse-engineering before every change
• Assumptions in people's heads → every change is a risk
Slide 4 (Consequence)
Months of "where does this come from?" before owning one improvement.
Slide 5 (Reframe)
Not a capability gap. A design gap.
Slide 6 (CTA)
Audit → map structure, document assumptions, roadmap for the new person. DM us.Outline
POST 2 — Why new analytics engineers take 3 months to ramp
FACTS / EVIDENCE:
- Monolithic models (600+ lines) make debugging and ownership hard; modular ~100-line units allow tracing to a specific slice
- DAG violations (e.g. mart feeding back to intermediate) force new hires to reverse-engineer the graph before making safe changes
- Undocumented assumptions (grain, exclusions, business rules) force dependency on the incumbent or risky changes
IMPLICATIONS:
- Ramp stretches to months of discovery instead of productive contribution
- Design was optimized for shipping, not for knowledge transfer
CTA (current): Audit to map structure, document assumptions, roadmap for the new person; DM us — Tier 4
Non-DM CTAs (suggested):
| Tier | CTA | What to set up |
|---|---|---|
| 1 | ”Read how we map structure and document assumptions so the next person has something to lean on: [tracked link].” | Tracked link (UTM or short link) to a blog or one-pager; log clicks. |
| 2 | ”We turned our ‘ramp readiness’ audit into a one-pager. Comment RAMP and I’ll send it.” Or gated form link. | Asset: one-pager on structure + assumptions. Form or comment-keyword delivery; log downloads. |
| 5 | ”If you’re bringing on a second analytics engineer in the next 90 days, book a 30-min audit preview: [booking link]. We’ll show you what we’d look at.” | Same booking link as Post 1 (dbt audit preview) or separate; attribute in form. |
Post 3 — How top teams document dbt for knowledge transfer
Pillar: Solution
Topic: How top teams document dbt for knowledge transfer
First Draft
Post
Your dbt project is fragmented by design. Models here, logic there, and “why we did it” in someone’s head. When that person leaves or you hire a second analytics engineer, the project doesn’t explain itself.
The fix isn’t more Confluence pages. It’s making the documentation live where the code lives, and making it answer the questions the next person will actually ask.
Here’s how teams that transfer knowledge well do it:
-
Document the logic of what you’re doing. Not just column names. The business rule. Why this join, why this filter. So when someone opens the model in six months they don’t have to infer intent from 200 lines of SQL.
-
State your assumptions explicitly. Grain of the table. What’s in scope and what’s out. What would break if the source changed. That’s part of the documentation too. People who look at it later need to know what the model assumes about the data.
-
Use naming and structure that reflect the data and the flow. Model names that reflect sources and what the model does. Staging → intermediate → marts. When the next person sees a filename they get a signal. When they see the DAG they see the story.
-
Define sources properly. Not hard-coded table names. Sources in sources.yaml so you can run freshness checks and so the next person knows where raw data lives and how fresh it’s expected to be. Teams that skip this lose the ability to monitor and to onboard cleanly.
Add it all up, and the codebase stops being a black box. New hires and new stakeholders can read the project and get to “what is what and why it was done” without a tribal-knowledge tour. That’s how top teams document dbt. Not as an afterthought. As the thing that makes the system maintainable.
If your dbt project would leave the next owner guessing, we can help. Our audits always include documentation and assumption clarity, and we often deliver both the audit and a documentation pass so the team has something to hand off. DM us if you want to talk through what that looks like for your stack.
Carousel
CAROUSEL 3 — How top teams document dbt for knowledge transfer
Slide 1 (Hook)
The project doesn't explain itself when someone leaves or you hire a second person.
Slide 2 (Reality)
Logic in heads, "why we did it" nowhere. Confluence isn't the fix.
Slide 3 (What works)
• Document the logic — business rules, not just columns
• State assumptions — grain, in/out of scope, what breaks if source changes
• Naming + structure — reflect data and flow (staging → intermediate → mart)
• Define sources — sources.yaml, not hard-coded names; freshness + clarity
Slide 4 (Outcome)
Next person gets "what is what and why" without a tribal-knowledge tour.
Slide 5 (Reframe)
Documentation isn't an afterthought. It's what makes the system maintainable.
Slide 6 (CTA)
Audit + documentation pass so you have something to hand off. DM us.Outline
POST 3 — How top teams document dbt for knowledge transfer
FACTS / EVIDENCE:
- Documentation should live with the code and answer “what is what and why it was done”
- Explicit assumptions (grain, scope, sensitivity to source changes) are part of documentation
- Naming and DAG structure (staging → intermediate → mart) give the next person clear signals
- Sources defined in sources.yaml enable freshness and clear onboarding; hard-coded names lose both
IMPLICATIONS:
- Codebase becomes readable and maintainable without tribal knowledge
- New hires and stakeholders can understand the project without a full tour from the original builder
CTA (current): Audit + documentation pass so the team has something to hand off; DM us — Tier 4
Non-DM CTAs (suggested):
| Tier | CTA | What to set up |
|---|---|---|
| 1 | ”See how we document logic and assumptions so the next owner isn’t guessing: [tracked link].” | Tracked link to a short piece or one-pager on documentation; log clicks. |
| 2 | ”We have a one-pager: how we document logic and assumptions in audits. Comment DOCS and I’ll send it.” Or: “Get the doc: [gated form link].” | Asset: one-pager on documentation in audits. Form or comment-keyword; log downloads. |
| 5 | ”Want to see what a documentation pass looks like for your stack? Book a 30-min walkthrough: [booking link]. No pitch, just what we’d look at.” | Same or separate dbt audit preview link; attribute. |
Post 4 — dbt testing strategy that prevents issues
Pillar: Solution
Topic: dbt testing strategy that prevents issues
First Draft
Post
dbt tests aren’t broken because you have the wrong tests. They’re broken because too many teams let failures become background noise.
We’ve seen it over and over. A test has been failing for years. Everyone knows. “We need to overlook it.” So the test stays, but nobody acts on it. New people don’t know if it’s real or legacy. When a real issue shows up, it’s buried in the same red. The testing layer stops protecting the business and starts adding confusion.
Here’s what actually prevents issues:
-
Tests that mean something. Uniqueness, not-null, relationships, custom logic that matches your business rules. If a test fails, there’s a clear action. Not “we’ve always had that failure.”
-
Source freshness. Define your sources and set freshness expectations. You find out when yesterday’s data didn’t land before someone asks why the dashboard is empty. Without it you’re debugging at 9 a.m. instead of catching it at 6.
-
No permanent “known failure” list. If a test is wrong, fix or remove it. If it’s right, fix the data or the model. Letting tests fail forever is the same as having no tests. It trains the team to ignore red.
The goal isn’t more tests. It’s tests that catch real problems and that the team actually trusts. When tests catch issues before they hit the dashboard, and when new hires can tell real failures from noise, you have a testing strategy that prevents issues instead of creating them.
This isn’t a tooling problem. It’s a discipline problem. Tests only work when the team commits to acting on what they find.
If your dbt project has tests that everyone ignores, we can help. Our audits always include test design and source freshness, and we help teams get to a state where red means “fix this” instead of “ignore this.” DM us if you want to talk through what that looks like.
Carousel
CAROUSEL 4 — dbt testing strategy that prevents issues
Slide 1 (Hook)
Tests aren't broken because you have the wrong ones. They're broken because failures became noise.
Slide 2 (Reality)
"This test has been failing for years. We need to overlook it."
Slide 3 (What works)
• Tests that mean something — clear action when they fail
• Source freshness — catch missing data before the business asks
• No permanent "known failure" list — fix or remove; red = act
Slide 4 (Consequence of bad)
New people can't tell real failures from legacy. Real issues get buried.
Slide 5 (Reframe)
Not a tooling problem. A discipline problem.
Slide 6 (CTA)
Audit includes test design + source freshness. Red = "fix this." DM us.Outline
POST 4 — dbt testing strategy that prevents issues
FACTS / EVIDENCE:
- Teams often leave tests failing for years and treat them as “known”; new hires can’t distinguish real issues from legacy noise
- Source freshness (sources.yaml + expectations) catches missing or stale data before stakeholders notice
- Tests only prevent issues when the team acts on failures; permanent “known failure” lists undermine trust
IMPLICATIONS:
- Testing layer either protects the business or adds confusion; discipline (fix or remove) matters more than volume
CTA (current): Audit includes test design + source freshness so red means “fix this”; DM us — Tier 4
Non-DM CTAs (suggested):
| Tier | CTA | What to set up |
|---|---|---|
| 1 | ”See how we design tests so red means ‘fix this’ (and what we look for in source freshness): [tracked link].” | Tracked link to a short piece on testing strategy; log clicks. |
| 2 | ”We wrote a short guide: how we set up tests and source freshness in audits so teams actually act on red. Comment TESTS and I’ll send it.” Or gated form. | Asset: testing + source freshness guide or one-pager. Form or comment-keyword; log downloads. |
| 5 | ”If your test suite sounds like this, book a 30-min audit preview and we’ll walk through how we’d get you to ‘red = fix this’: [booking link].” | Same dbt audit preview link; attribute. |
Post 5 — What we audit in a dbt code review
Pillar: Service
Topic: What we audit in a dbt code review
First Draft
Post
When we do a dbt audit, we’re not just looking for “bad code.” We’re looking for the same things that make the difference between a codebase one person can barely hold in their head and one a team can own and improve.
Here’s what we actually check:
-
DAG integrity. Staging → intermediate → mart. No cycles. We’ve seen mart models feeding back into intermediate. That breaks the idea of an acyclic graph and makes every change risky. We map the real flow and call out violations.
-
DRY. Don’t repeat yourself. The same calculation in five places means five places to update and five ways to drift. We find repeated logic and flag where it should live once.
-
Modularity. Models around 100 lines, not 600. Long files are where “something’s wrong in line 585” comes from. We look for monoliths that should be split by business logic so the next person can debug and change safely.
-
Documentation and assumptions. Is the logic documented? Are assumptions (grain, scope, dependencies) stated? Without that, the only way to understand the system is to read every line. We note where docs are missing or vague.
-
Testing and source freshness. Do tests exist and do they catch real issues? Are sources defined so you can run freshness checks? We’ve seen stacks with no source definitions and no way to know if yesterday’s data made it in. We assess what’s there and what’s missing.
-
Naming and materialization. Do names reflect sources and purpose? For large tables, is the project using incremental materialization where it makes sense, or rebuilding everything every run and burning time and compute? We call out optimization and naming improvements.
We package this into a clear report and a prioritized roadmap. So you get “here’s what we found” and “here’s what we’d do first,” not a pile of notes. If you’re about to hire a second analytics engineer, or your dbt runs are taking half the night and you don’t know where to start, an audit is the fastest way to get a plan. DM us if you want to talk through scope and what you’d get back.
Carousel
CAROUSEL 5 — What we audit in a dbt code review
Slide 1 (Hook)
We're not just looking for bad code. We're looking for what makes a codebase ownable.
Slide 2 (Reality)
One person can barely hold it in their head vs. a team can own and improve.
Slide 3 (What we check)
• DAG integrity — staging → intermediate → mart, no cycles
• DRY — repeated logic flagged, single source of truth
• Modularity — ~100 lines, not 600-line monoliths
• Documentation + assumptions — logic and grain stated
• Testing + source freshness — tests that matter, sources defined
• Naming + materialization — incremental where it helps
Slide 4 (Deliverable)
Report + prioritized roadmap. What we found, what we'd do first.
Slide 5 (CTA)
Hiring a second person or dbt runs eating the night? Audit = fastest plan. DM us.Outline
POST 5 — What we audit in a dbt code review
FACTS / EVIDENCE:
- DAG: staging → intermediate → mart, no cycles (e.g. mart → intermediate)
- DRY: repeated logic leads to drift and multiple update points
- Modularity: ~100-line models vs 600+ line monoliths for debuggability
- Documentation and assumptions: logic and grain documented so the next person can understand
- Testing and source freshness: meaningful tests, sources in sources.yaml for freshness
- Naming and materialization: names that reflect purpose; incremental where it saves time and compute
IMPLICATIONS:
- Audit produces a report and prioritized roadmap, not just a list of issues
- Useful before hiring a second analytics engineer or when dbt runtimes are a problem
CTA (current): DM us to talk through scope and what you’d get back — Tier 4
Non-DM CTAs (suggested):
| Tier | CTA | What to set up |
|---|---|---|
| 1 | ”See the 6 things we actually check in a dbt audit and what we deliver: [tracked link].” | Tracked link to one-pager or blog on audit scope + deliverable; log clicks. |
| 2 | ”We turned our audit checklist into a one-pager: the 6 things we check and what you get back. Comment AUDIT and I’ll send it.” Or gated form. | Asset: audit checklist one-pager. Form or comment-keyword; log downloads. |
| 5 | ”Not ready for a full audit? Book a 30-min ‘what we’d look at’ call. You leave with a short list of where we’d start: [booking link].” | Dedicated “audit preview” or “scope call” link; attribute. |
Post 6 — dbt audit → roadmap (case study)
Pillar: Service
Topic: dbt audit → roadmap (case study)
First Draft
Post
Here’s what we actually do when we run a dbt audit and turn it into a roadmap.
We don’t rip out the existing stack and start over. The team is still shipping. We come in parallel. We get access to the repo and the warehouse, we map how the system really works, and we produce an audit report and a prioritized roadmap. “Here are the problems. Here’s what we’d fix first, second, third. Here’s where you’re losing time and where the next person will get stuck.”
Then the client chooses. Some only want the roadmap. They use it to hire or to plan the next quarter. Others want us to implement. In that case we scope by what hurts most. “Give us your most pressing data mart, give us 6 weeks, and we’ll tackle it.” Revenue, sales, marketing, inventory. They pick. We deliver against the roadmap we already built.
We’ve done this with teams that had one analytics engineer and a codebase that had grown under “we need things to happen” pressure. Magic Spoon was one. We audited, found bottlenecks and redundant logic, and identified where runtimes could come down (in that case from hours to something much closer to what the business needed). Another client, Urban Stems, had a solo data owner who couldn’t stop daily work to refactor. We audited, built a parallel cleaner infrastructure alongside the existing one, and then migrated them over. So they kept running while we fixed the foundation.
The pattern is the same. Audit first. Roadmap second. Then either they run with the roadmap or we implement the highest-priority slice. No big-bang rewrite. No “throw everything away.” Just a clear picture of what’s wrong and a path to fix it in order.
If you’re sitting on a dbt project that feels clunky, or you’re about to bring on a second person and don’t want them to spend three months in the weeds, this is the motion. DM us and we can walk through what an audit would look like for your stack and what you’d get back.
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CAROUSEL 6 — dbt audit → roadmap (case study)
Slide 1 (Hook)
We don't rip and replace. We audit, roadmap, then you choose.
Slide 2 (Reality)
Team keeps shipping. We map the system and produce report + prioritized roadmap.
Slide 3 (What we deliver)
• Audit: problems, bottlenecks, where time is lost, where next person gets stuck
• Roadmap: what we'd fix first, second, third
• Optional: "Give us your most pressing data mart, 6 weeks" — they pick, we implement
Slide 4 (Case: Magic Spoon)
Audit → bottlenecks and redundant logic → runtimes from hours to what the business needed.
Slide 5 (Case: Urban Stems)
Solo owner couldn't stop to refactor. We built parallel infrastructure, then migrated. No downtime.
Slide 6 (Reframe)
No big-bang rewrite. Clear picture, path to fix in order.
Slide 7 (CTA)
dbt feels clunky or you're hiring a second person? DM us for what an audit would look like.Outline
POST 6 — dbt audit → roadmap (case study)
FACTS / EVIDENCE:
- Process: audit in parallel (team keeps shipping) → report + prioritized roadmap → client chooses roadmap-only or implementation
- Implementation scoped by data mart (revenue, sales, marketing, inventory); e.g. “most pressing data mart, 6 weeks”
- Magic Spoon: audit found bottlenecks and redundant logic; runtimes reduced toward what business needed
- Urban Stems: solo data owner; we built parallel cleaner infrastructure and migrated; no stop-the-world refactor
IMPLICATIONS:
- No big-bang rewrite; clear problems and ordered path to fix
- Works when team can’t pause daily work to refactor or when bringing on a second person
CTA (current): DM us to walk through what an audit would look like for your stack and what you’d get back — Tier 4
Non-DM CTAs (suggested):
| Tier | CTA | What to set up |
|---|---|---|
| 1 | ”See what an audit looks like and what you get back (report + roadmap, then optional implementation): [tracked link].” | Tracked link to case study or one-pager (e.g. Urban Stems / Magic Spoon flow); log clicks. |
| 3 | ”We’re running a webinar on how we go from audit → roadmap → implementation. Register here: [event link].” (If you have or plan a dbt/audit webinar or workshop.) | Event page (Luma, Eventbrite, etc.); count signups. |
| 5 | ”If you’re sitting on a clunky dbt project or hiring a second person soon, book a call and we’ll walk you through what an audit looks like and what you’d get back: [booking link].” | Same dbt audit preview / scope call link; attribute. |
Context for you (Robert)
If you need more from me:
- How we run dbt audits in practice: I used Demi’s description (access, repo, map DAG/docs/tests, report, roadmap) and the consulting-patterns “audit then implementation” and “scope by data mart.” If you have a different standard sequence (e.g. number of weeks, deliverables), share it and I’ll align the posts.
- Urban Stems / Magic Spoon specifics: I used “parallel infrastructure then migrate” for Urban Stems and “bottlenecks, redundant logic, runtimes” for Magic Spoon. If you have a client-approved outcome (e.g. “2.5hr → 1.5hr”) or a different story (e.g. Emily by name, one-man team), I can drop it in and tighten the case study.
- Eden / 5–6am dashboards: Referenced in the transcript; I didn’t name Eden in the posts to avoid over-relying on one example. I can add a light Eden mention if you want another named reference.
- Inteleos: SOW has dbt migration from Talend; that’s a different motion (migration from another tool). I left it out of this sequence so we don’t blur “dbt audit of existing dbt” with “migrate to dbt.”
Formats used:
- Posts 1–2: Diagnostic List (contrarian paradox / direct claim, numbered diagnosis, consequence, reframe, relational CTA).
- Post 3: Silo-to-Signal style (fragmentation → what good looks like → transition → outcomes → CTA).
- Post 4: Problem → Common Fix → Better Fix (tests “broken” → common fix of adding tests vs discipline of acting on failures; source freshness as better fix).
- Post 5: B2B Framework / teaching list (what we check, deliverable, CTA).
- Post 6: Process Reveal + case study (here’s what we do, cases, no big-bang, CTA).
All avoid generic dbt advice and use vault-specific wins and interview evidence. If you want a different format for any pillar (e.g. more vulnerable/story for one of the problem posts), say which number and I’ll redraft.