Multi-Agent DBT Development Workflow for Data Engineering Consulting

mammoth_growth · Consulting · 2026 · source Agentic: yes · promotional bias: med · source-backed · extracted with prompt 0.5.6 (schema v2)

Generated from extraction.json — do not edit by hand. Every claim cites verbatim source evidence; stated means the quote directly asserts the claim, implied means it supports it indirectly.

Problem

Domain: Data engineering consulting

Mammoth Growth needed to deliver DBT analytics lineages faster while maintaining consulting-quality requirements, code, documentation, and tests.

Constraints:

Business requirements change faster than manual data teams can deliver.
DBT development must preserve quality, documentation, and tests rather than produce throwaway code.
Agents needed DBT-native command access to build, test, show, and repair models.
Human-led requirements and code review remained mandatory before production deployment.

Architecture

Mammoth Growth created a multi-agent DBT development workflow with orchestrator, analyst, architect, and analytics-engineer agents. The Analyst Agent profiles raw data through DBT commands, the Architect Agent turns specifications into milestones and checklists, and the Analytics Engineer Agent writes, documents, tests, and repairs DBT models through the DBT MCP.

Diagram confidence: reconstructed (reconstructed from prose).

flowchart LR
  human-review["Human Review"] --> orchestrator-agent["Orchestrator Agent"]
  orchestrator-agent --> analyst-agent["Analyst Agent"]
  orchestrator-agent --> architect-agent["Architect Agent"]
  orchestrator-agent --> analytics-engineer-agent["Analytics Engineer Agent"]
  analyst-agent --> dbt-mcp["DBT MCP"]
  analyst-agent --> architect-agent
  architect-agent --> analytics-engineer-agent
  analytics-engineer-agent --> dbt-mcp

Drag to pan · scroll or use +/− to zoom · reconstructed from the source's described component topology

Components

Component	Role	Interacts with
`Orchestrator Agent`	Coordinates task distribution and handoffs across the DBT workflow.	`Analyst Agent`, `Architect Agent`, `Analytics Engineer Agent`
`Analyst Agent`	Profiles raw data, identifies relationships, and documents findings.	`DBT MCP`, `Architect Agent`
`Architect Agent`	Converts specifications into milestones, checklists, and coding instructions.	`Analytics Engineer Agent`
`Analytics Engineer Agent`	Writes, documents, tests, and self-corrects DBT models.	`DBT MCP`
`DBT MCP`	Allows agents to run DBT commands, inspect projects, and validate output.	`Analyst Agent`, `Analytics Engineer Agent`
`Human Review`	Provides requirements and mandatory code review before production deployment.	`Orchestrator Agent`

Data flow

Human consultants gather requirements and specifications. The Orchestrator Agent coordinates the workflow, the Analyst Agent queries raw source data and documents findings, the Architect Agent decomposes the spec into concrete milestones, and the Analytics Engineer Agent iteratively writes DBT code, runs tests, fixes failures, and submits work for human review.

“The Orchestrator Agent functions as the quarterback of the workflow, managing task distribution and coordination between all other agents.”summary, stated

“The Analytics Engineer (AE) Agent is the executor that writes, documents, and tests production-grade DBT code.”summary, stated

Why this architecture

The system mirrors a consulting data team while giving agents DBT-native execution ability. Separating analyst, architect, and analytics-engineer roles helped keep the coding agent on rails, and MCP converted DBT development into an autonomous loop rather than copy-paste prompts.

“The solution centers on a specialized team of AI agents that mirror the roles found in traditional data engineering teams.”summary, stated

“The MCP allows agents to interact with DBT projects programmatically, enabling them to build models, execute tests, iterate on failures, and validate output”summary, stated

Models used: unspecified coding LLMs

Scale

A pilot delivered 15 data models and two gold-layer models.
“The system enabled the delivery of a complete enterprise-grade data lineage with 15 data models and two gold-layer models in just 3 weeks”summary, stated
The pilot took three weeks compared with an estimated ten weeks manually.
“in just 3 weeks for a pilot client, compared to an estimated 10 weeks using traditional manual development approaches”summary, stated
Sequence-level discovery ran in parallel with human requirements gathering.
“This automated discovery process runs in parallel with human requirements gathering, significantly compressing project timelines.”summary, stated

Alternatives considered (roads not taken)

Manual DBT development

Rejected because: Traditional work was estimated at ten weeks for the pilot compared with three weeks using the agent workflow.

“compared to an estimated 10 weeks using traditional manual development approaches”summary, stated

Unstructured coding-agent prompting

Rejected because: The architect's detailed milestones and examples kept the coding agent on rails.

“This explicit breakdown proved critical to keeping the coding agent "on the rails" and preventing hallucinations or deviation from requirements.”summary, stated

Manual copy-paste between LLM and DBT

Rejected because: DBT MCP enabled agents to build, test, iterate, and validate directly.

“This capability transforms what would otherwise be a series of manual copy-paste operations into a truly autonomous development loop.”summary, stated

Patterns

`multi-agent-orchestration` (architecture)

Orchestrator, analyst, architect, and analytics-engineer agents divide DBT delivery work.

“The solution employs a team of specialized AI agents (orchestrator, analyst, architect, and analytics engineer)”summary, stated

`tool-use-orchestration` (execution)

DBT MCP lets agents run DBT commands, query data, build models, and run tests.

“It uses standard DBT commands including build, run, test, and show to develop models iteratively.”summary, stated

`human-in-the-loop-gate` (safety)

Requirements gathering and mandatory review remain human-led before production deployment.

“maintaining code quality standards through human-led requirements gathering and mandatory code review before production deployment.”summary, stated

Tradeoffs

Use role-specialized agents

Chose Analyst, architect, and analytics-engineer agents over One general DBT coding agent.

Rationale: The roles mirror human data-engineering teams and keep each agent scoped.

“Each agent has distinct responsibilities and works collaboratively through orchestration:”summary, stated

Keep humans at requirements and review boundaries

Chose Human-led requirements gathering and mandatory code review over Fully autonomous production deployment.

Rationale: The system accelerates development but preserves quality gates before production.

“mandatory code review before production deployment.”summary, stated

Gotchas (what bit them)

Coding agents need detailed plans to avoid drifting from requirements.

Root cause: DBT work involves business definitions, dependencies, and data quality constraints.

Resolution: The Architect Agent decomposes specifications into explicit milestones and checklists.

“The Architect Agent serves as the strategic planner, taking detailed textual specifications and decomposing them into explicit milestones and checklists.”summary, stated

DBT automation needs execution feedback, not just generated code.

Root cause: Generated models can fail tests or require data-aware logic changes.

Resolution: The AE agent runs tests, queries data, updates logic, and re-runs tests until checks pass.

“the AE agent autonomously fix its own failing tests by querying data, updating the model logic, and re-running tests until all checks passed.”summary, stated

Evaluation

The case uses pilot delivery time, model counts, tests, and human review as evidence of workflow quality.

Metrics: 15 data models · two gold-layer models · 3 weeks versus estimated 10 weeks

“The system enabled the delivery of a complete enterprise-grade data lineage with 15 data models and two gold-layer models in just 3 weeks”summary, stated

Outcomes

Mammoth Growth compressed an enterprise DBT pilot from an estimated ten weeks to three weeks.
The system generated, documented, tested, and corrected production-grade DBT code.
DBT MCP was the enabling interface for autonomous development loops.

The pilot delivered 15 data models and two gold-layer models in three weeks.

“This self-correction capability demonstrated the system moving beyond simple code generation toward genuine autonomous development.”summary, stated