Data Engineering

Legacy CRM Consolidation for Professional Sports League Using Snowflake Secure Data Sharing

The Situation

A professional sports league was migrating its member clubs onto a centralised, league-managed Salesforce CRM platform to standardise how fan and member data is managed across the competition. One club presented a problem no standard integration tool could solve.

  • Its marketing preferences data lived in a legacy customer data platform built specifically for sports organisations, structurally incompatible with the integration layer used for every other club
  • A second legacy CRM platform added a further migration workstream with its own complex schema analysis requirements
  • The league's internal team had the domain knowledge but not the data engineering capacity to execute the transformation work
  • A third-party integration platform had been considered, but the client determined that a skilled InterWorks data engineer was the better path forward

What We Did

InterWorks embedded a data engineer directly into the league's team to execute both migration workstreams using Snowflake as the transformation layer throughout.

  • Configured Snowflake Secure Data Sharing between the club's Snowflake instance and the league's instance, enabling transformation work to happen on the target environment without moving or duplicating raw data
  • Built comprehensive scenario matrices capturing all field mapping permutations across email and SMS marketing preference types, then used Claude AI to generate multi-step SQL transformation scripts from those matrices
  • Designed both full and incremental migration logic to ensure preference changes made during the cutover window were captured without data loss
  • Delivered completed, validated data to the league's CRM team for final load into Salesforce Marketing Cloud
  • Analysed hundreds of fields across accounts and contacts objects in the second legacy CRM to determine Salesforce mapping relevance, using Claude AI to compare schema definitions and surface semantic differences between systems
  • Loaded dozens of manually extracted account and contact sheets to Snowflake, de-duplicated records, and added analysis columns to support informed migration decisions
  • Identified and resolved root-cause data quality issues in already-migrated Salesforce data that had been silently affecting marketing segmentation and email targeting across multiple clubs
  • Provided knowledge transfer on Snowflake analytical approaches so the league team could continue remaining migration work independently

Results

  • Migration completed with zero data quality issues; all client validation tests passed
  • Club decommissioned its legacy customer data platform and moved fully to the league-managed Salesforce environment
  • Marketing operations resumed within days of go-live
  • AI-assisted development reduced complex SQL build time compared to manual development
  • Systemic data quality issues affecting multiple clubs across the competition identified and resolved as a byproduct of the engagement

What This Unlocks

For sports organisations, entertainment businesses, or any multi-entity operation running a shared CRM platform, this engagement demonstrates how Snowflake Secure Data Sharing can serve as a transformation intermediary between organisational instances, removing the need for direct data transfers while enabling complex field mapping and migration logic.

  • Snowflake Secure Data Sharing works as a clean, auditable bridge for cross-entity migrations where both parties already operate on Snowflake
  • Structured scenario matrices combined with AI-assisted SQL generation is a repeatable pattern for high-volume, logic-intensive migrations that standard integration tools cannot address
  • The approach is directly applicable to any sports league, franchise network, or multi-entity business consolidating CRM or marketing data into a shared platform

Team

Led by an embedded data architect and engineer who served as the primary delivery resource across both migration workstreams, supported by a solutions architect providing delivery oversight.

Back to Snowflake Use Cases
Snowflake Secure Data SharingSalesforce migrationCRM data migrationAI-accelerated developmentdata engineeringsports mediamulti-entity architectureAPAC

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