Building Your RevOps Reporting Stack From Scratch

Most RevOps teams outgrow CRM-native reporting within their first year. The dashboards are slow, cross-object reporting is limited, and combining CRM data with marketing or product usage data is impossible without exporting to spreadsheets. Building a proper reporting stack solves these problems, but the architecture decisions you make early on determine whether your stack scales cleanly or collapses under its own weight.

The Four Layers of a Reporting Stack¶

Every reporting stack, regardless of tool choices, follows the same four-layer architecture:

Layer 1: Extraction¶

Your first decision is how to get data out of your CRM and into a warehouse. There are two approaches:

1. Managed ETL/ELT (Fivetran, Airbyte) - Prebuilt connectors sync CRM data on a schedule. Fivetran is the most reliable for Salesforce; Airbyte is a strong open-source alternative.
2. Reverse ETL (Census, Hightouch) - Pushes warehouse data back into operational tools. Useful when your warehouse becomes the source of truth for enriched data.

Practical tip: Start with a 15-minute sync interval for pipeline data and daily syncs for everything else. Real-time replication sounds appealing but rarely justifies the cost for RevOps reporting.

Layer 2: Storage¶

For most RevOps teams, BigQuery or Snowflake is the right choice:

• BigQuery: Best if your company is already on Google Cloud. Pay-per-query pricing keeps costs low for small teams. No infrastructure to manage.
• Snowflake: Best for multi-cloud environments. Excellent performance on large datasets. Usage-based pricing with more granular cost controls.

Budget expectation: A typical RevOps dataset (CRM + marketing + support) costs $50-200/month in warehouse compute. This is not the budget-buster that many teams fear.

Layer 3: Transformation¶

Raw CRM data is messy. Opportunity stage names change, fields get renamed, and deleted records linger. dbt (data build tool) has become the industry standard for transformation because it:

• Lets you write transformations in SQL, which RevOps analysts already know
• Version-controls your data models in Git
• Generates documentation automatically
• Tests data quality with built-in assertions

Key models to build first:

1. dim_accounts - Deduplicated, enriched account master
2. fct_opportunities - Cleaned opportunity fact table with standardized stages
3. fct_pipeline_snapshots - Weekly point-in-time pipeline snapshots for trend analysis
4. dim_reps - Rep/manager hierarchy with territory assignments

Layer 4: Presentation¶

For most RevOps teams under 200 reps, Mode or Metabase delivers 90% of what Looker does at a fraction of the cost. Looker’s value shines when you need a governed semantic layer across multiple departments.

A Practical Starting Architecture¶

If you are building from scratch today, here is the lowest-friction path:

1. Fivetran to sync Salesforce and HubSpot data to BigQuery
2. dbt Cloud (free tier) to transform raw data into clean models
3. Metabase or Mode for dashboards
4. Total cost: approximately $500-800/month before scaling

Key Takeaways¶

• Structure your stack in four layers - extraction, storage, transformation, presentation - and choose tools for each independently
• dbt has become the standard transformation layer for RevOps; invest time learning it early
• Start with Metabase or Mode unless you have a specific need for Looker-level governance
• Budget $500-800/month for a production-quality reporting stack at the mid-market level