Data Cleaning and Governance: HubSpot Data Migration

Marketing and sales data lived in silos, HubSpot exports in one corner, financial reports in another. During migration, leadership faced a critical risk: what if records were lost, mismatched, or couldn’t be tied back to revenue? Without reliable alignment, campaign ROI was unknowable, budgets could be wasted, and forecasts missed. Lunexa Insights executed a seamless migration and integration, ensuring every touchpoint, transaction, and dollar flowed into a unified, trusted dataset. The result: leadership could measure ROI with confidence and scale campaigns without fear of broken data.

Context

Marketing and sales teams relied on fragmented HubSpot exports and separate financial reports to gauge campaign ROI. Without a unified view of marketing touchpoints alongside transaction volume, revenue, and average order value (AOV), it was impossible to tell if spend and activity were delivering against forecasts.

Objective

Migrate HubSpot into a governed Postgres warehouse via Stitch to unify marketing touchpoints with revenue and AOV.

Role

Senior Data Analyst and Lead Data Architect responsible for designing, building, and validating the end-to-end pipeline.

Duration

4 weeks

Tools

ETL: Stitch for nightly incremental API extraction from HubSpot
Warehouse: PostgreSQL (raw_hubspot & analytics schemas)
Transform & Tests: dbt for staging models, core transforms, and data validation
Governance & Monitoring: Version-controlled field mapping spec; Slack alerts on sync or test failures

The Approach and Process

Methodology & Process

Field Discovery & Mapping

Audited HubSpot objects (Contacts, Deals, Companies, Campaigns).
Defined mapping for key marketing fields (campaign ID, source/medium, ad spend) and financial fields (deal amount, close date, transaction count, AOV).

ETL Configuration with Stitch

Set up Stitch to pull HubSpot data via API every night.
Enabled incremental sync to capture new/updated records; full refresh on schema changes.
Loaded raw tables (raw_contacts, raw_deals, raw_campaigns) into the raw_hubspot schema in Postgres.

dbt Modeling & Cleaning

Staging Models: Cast raw fields to correct types, dedupe records, parse dates and currency.
Core Models:
- dim_campaign, dim_contact, dim_deal for lookup tables.
- fct_marketing_performance: joined campaigns to deal records to compute transaction counts, total revenue, and AOV by campaign.
- Added planned vs. actual fields pulled from HubSpot deal properties (e.g., planned spend, target AOV).

Validation & QA

Implemented dbt tests on nullability (e.g., every deal must link to a campaign), uniqueness, and value ranges (e.g., AOV > $0).
Integrated alerts so failures notify the data team via Slack immediately.

Analytics Publishing

Published clean performance tables into the analytics schema:
- analytics.campaign_roi, analytics.revenue_tracking, analytics.aov_trends.
Enabled BI tools to directly query these tables for real-time dashboarding.

Monitoring & Governance

Monitored Stitch sync success rates and dbt test results.
Documented field-mapping rules in Git; scheduled quarterly audits of key marketing and financial fields

End Results & Future Considerations

Future Considerations

Real-time Updates: Evaluate webhook-driven pipelines for sub-daily freshness.
Reverse ETL: Sync key performance metrics back into HubSpot for campaign optimization workflows.
Advanced Analytics: Develop predictive models for campaign performance and spend allocation.

Conclusion

By deploying a Stitch-driven ETL into Postgres and layering dbt transformations, the organization gained a single source of truth combining HubSpot marketing data with transaction, revenue, and AOV metrics, empowering rapid, data-driven decision-making against planned

goals.

Company