Auto-ETL with Agents: Taming Messy SaaS Data

Learn how auto-ETL with AI agents can clean and sync messy SaaS data (HubSpot, Stripe, Salesforce & more) into sane, trustworthy analytics pipelines — without constant fire drills.

You don’t really notice how messy your SaaS data is…
…until the CEO asks, “So what’s our MRR, exactly?” And suddenly you have three dashboards, four CSVs, and zero confidence. Let’s talk about a different way to do ETL: one where agents handle the chaos and your pipelines quietly heal themselves instead of breaking at 3 a.m.

The reality of SaaS sprawl (and why your metrics don’t match) Even in a “small” startup, your data lives in a dozen places:

• Stripe for billing
• HubSpot for marketing
• Salesforce for sales
• Zendesk or Intercom for support
• Product analytics for events
• A random Google Sheet someone swears is “the source of truth”

Each tool has its own schema, naming conventions, and ideas of what a “customer” is. On Monday, finance says MRR is $420k.
On Tuesday, growth says it’s $395k.
On Wednesday, someone quietly stops sending numbers in Slack. Let’s be real: the problem usually isn’t “no data.”
It’s that the pipelines gluing these SaaS tools together are brittle, opaque, and constantly one schema change away from breaking.

Why traditional ETL cracks under SaaS reality Classic ETL (or ELT) assumes a world where:

• Schemas change infrequently
• Pipelines are designed once, then carefully maintained
• Engineers have time to keep dbt models, Airflow DAGs, and contracts in sync

In SaaS land, reality looks more like:

• A new “custom field” added in Salesforce on Friday breaks a join on Monday
• Someone renames a lifecycle stage in HubSpot and your funnel just… evaporates
• Enrichment vendors silently change response formats
• You discover 12 variants of customer_id, each with slightly different semantics

Traditional pipelines handle this with more code, more tests, more manual fixes.
Which works — until the number of tools, teams, and “hotfixes” crosses some invisible line and the whole thing becomes unreasonably fragile. This is where auto-ETL with agents comes in.

What “auto-ETL with agents” actually means When people say “auto-ETL,” they often mean “we wrote more clever scripts.” Agent-based auto-ETL is different: instead of hard-coding every rule up front, you introduce LLM-powered agents that can:

• Reason about schemas and reconcile differences
• Propose or apply transformations based on intent (“normalize all revenue to USD, monthly cadence”)
• Detect anomalies and try to fix them (or at least explain them)
• Adapt to minor upstream changes without human intervention

You still define contracts, guardrails, and target models.
But instead of hand-authoring every mapping and step, you ask agents questions like: “Take Stripe + Salesforce + HubSpot, define a unified Account model, and keep it in sync daily. Show me how you’ll do it.” Then you review, constrain, and monitor what they generate.

A mental model: agents around a stable core Here’s a simple way to picture it:

[ SaaS APIs ]
  (Stripe, HubSpot, SFDC, etc.)
              |
              v
     [ Ingestion Agent(s) ]
              |
          [ Raw/Staging ]
              |
              v
     [ Transform Planner Agent ]
              |
    +-----------------------------+
    |  Generated dbt / SQL /     |
    |  Python transform steps    |
    +-----------------------------+
              |
         [ Validation Agent ]
              |
              v
       [ Warehouse / Lake ]
              |
              v
          [ BI / Metrics ]

The core (warehouse, base models, metrics layer) stays as deterministic and boring as possible. Agents orbit this core, handling:

• Schema inference and mapping
• Transform generation and refactoring
• Anomaly detection and suggestions
• “Explain why this changed” tasks for humans

You’re not replacing proven tools like dbt or Airflow — you’re adding a layer that writes, maintains, and adapts those assets for you.

A concrete example: one revenue number, not three Imagine a SaaS company with:

• Stripe as billing system
• Salesforce as CRM
• HubSpot as marketing source
• A warehouse (Snowflake/BigQuery) + dbt for transforms

The problem:
Revenue reports disagree. Finance trusts Stripe. Sales trusts Salesforce. Growth trusts a Looker dashboard that nobody understands anymore. Step 1 — Define the stable target You define a clear target model:

• dim_account
• fct_subscription
• fct_revenue_daily

Plus business rules:

• All amounts in USD
• Revenue recognized daily from start to end of subscription
• Cancellations and downgrades handled as adjustments

Step 2 — Let agents handle the messy upstream bits Agents can:

• Inspect Stripe, Salesforce, and HubSpot schemas
• Propose mappings like “Stripe customer → Salesforce Account via email + domain heuristic”
• Generate dbt models to implement the logic
• Add tests: uniqueness, referential integrity, non-null revenue, etc.

You review the plan, tweak any business logic, and approve. Step 3 — Recovery when things change Two months later, someone adds a new Salesforce billing field and changes how opportunities map to subscriptions. Instead of a silent break, the agent notices:

• A schema diff in Salesforce
• A drop in test pass rates for fct_revenue_daily

It proposes:

• Updated mappings
• A refactor of the join logic
• A summary: “Here’s how your revenue logic would change.”

You can choose: auto-apply for low-risk changes, or require approval for anything touching core metrics.

Architecture deep dive: what you actually need Let’s unpack the main components. 1. Ingestion & schema agents You still use connectors (Fivetran, Airbyte, custom scripts). Agents sit on top to:

• Infer and document schemas in a consistent format
• Detect “isomorphic” fields across tools (e.g., company, account, organization)
• Propose a canonical data model for entities like Account, Contact, Subscription

2. Contract & metadata layer This part is not optional. You need:

• Entity definitions: what is an Account, exactly?
• Metric contracts: definition of MRR, churn, LTV, etc.
• Data contracts with upstream teams (e.g., “we will always send billing_currency”)

Agents use these contracts as constraints. They’re not dreaming up pipelines; they’re optimizing within your rules. 3. Transform planner & code generator Here, agents:

• Take your target model + current schemas
• Generate or update transformation code (dbt SQL, Python, etc.)
• Run dry-runs and unit tests
• Explain plans in human language

A simple pseudo-example in Python:

from typing import List
from pydantic import BaseModel

class TransformRequest(BaseModel):
    target_model: str
    description: str
    upstream_tables: List[str]

class TransformResponse(BaseModel):
    sql: str
    tests: List[str]
    notes: str

def generate_transform(agent_llm, req: TransformRequest) -> TransformResponse:
    prompt = f"""
    You are a data engineer. Create a dbt model for {req.target_model}.

    Description: {req.description}

    Use these upstream tables: {", ".join(req.upstream_tables)}.
    Return:
    1) SQL for the model
    2) A few dbt tests (as YAML snippets)
    3) Short notes on assumptions made.
    """
    raw = agent_llm(prompt)  # abstract LLM call
    return TransformResponse.parse_raw(raw)

In practice, you’d add:

• Schema introspection
• Safety checks (no SELECT * from prod sources, etc.)
• Integration with your git/CI setup

4. Validation, monitoring, and feedback This is where “auto” becomes safe:

• Agents monitor test results and freshness
• When something degrades, they:
• Summarize the issue
• Propose fixes
• Estimate risk based on which models are impacted
• You can give feedback (“this mapping is wrong”), which the agent uses to update future plans

Guardrails: keeping “auto” from becoming chaos If you’re getting nervous about agents rewriting your revenue pipeline, good. You should be. Here are non-negotiable guardrails:

• Read-only by default: agents propose changes via PRs, not direct writes to prod
• Approval flows for high-impact models (revenue, churn, KPIs)
• Canary runs: new logic runs in parallel for a period, then you compare metrics
• Lineage-aware alerts: when something breaks, you see upstream causes, not just red boxes
• Human-readable diffs: agents must explain “what changed and why it matters” in plain language

The goal is auto-ETL as copilot, not a mysterious black box.

When auto-ETL with agents makes sense (and when it doesn’t) You probably should explore agentic auto-ETL if:

• You rely on 5+ SaaS tools as core systems of record
• Your definitions of “customer,” “account,” and “MRR” change as the business matures
• Data engineers are stuck doing map-and-rename work instead of higher-leverage modeling
• You keep discovering “shadow pipelines” in notebooks and Sheets

You probably shouldn’t bother (yet) if:

• You have one main SaaS system and minimal integration needs
• Your schema is relatively stable and your team is small
• You don’t have basic data hygiene in place (tests, access control, versioning)

Agents amplify whatever process you already have — good or bad.

Wrap-up: make your pipelines boring again Auto-ETL with agents isn’t about chasing another buzzword. It’s about admitting that modern SaaS data is too messy, dynamic, and cross-functional to manage with only static pipelines and heroic weekend fixes. Instead, you:

• Define stable contracts and metrics
• Let agents handle the grunt work of mapping, generating, and refactoring transforms
• Add strong guardrails so “auto” never means “out of control”
• Reserve human attention for the judgment calls that truly matter

If you’re wrestling with conflicting dashboards or brittle SaaS integrations, start small: pick one messy flow (like revenue or lifecycle stages), and experiment with an agentic layer around your existing stack. You might be surprised how quickly “we don’t trust the data” turns into “we finally have one number everyone agrees on.”

If this resonated, I’d love to hear:
What’s the messiest SaaS data flow in your company right now? Drop it in the comments, follow me for more deep dives on AI + data workflows, and feel free to share this with the one person in your org who’s secretly maintaining that critical-but-terrifying spreadsheet.

Read the full article here: https://medium.com/@connect.hashblock/auto-etl-with-agents-taming-messy-saas-data-6e8afb6fb040