Build · 4 weeks

Salesforce Data Cloud Foundation

Azure-Salesforce Data Cloud integration deployed in 4 weeks. Bidirectional data sync, customer 360 profiles, AI-powered lead scoring, and governed pipelines connecting Databricks, Snowflake, and AWS S3 to Salesforce CRM.

View foundation on GitHub

Week 1 — Design

Integration pattern selection (MuleSoft vs Data Factory)
Customer 360 data model design
Salesforce Data Cloud connected app and OAuth configuration
Data mapping — source systems to unified customer profile
Security model — Key Vault, private endpoints, consent management

Weeks 2-3 — Build

Azure infrastructure via Terraform (ADLS Gen2, Data Factory, OpenAI)
Salesforce-to-Azure ingestion pipelines (CDC or Bulk API)
dbt models — staging, customer 360, lead scoring marts
Azure OpenAI enrichment — lead scoring and next-best-action
Azure-to-Salesforce writeback for enriched data

Week 4 — Operationalize

Pipeline monitoring and failure alerting
Data quality checks on customer profiles
Cost management — compute policies, API call budgets
Runbook and knowledge transfer
Roadmap for additional Salesforce objects and data sources

Deliverables

What you walk away with

Bidirectional Data Sync

Salesforce ↔ Azure pipelines running in production — CDC or batch, your choice.

Customer 360 Profiles

Unified customer view in your data lake combining Salesforce, ERP, and behavioral data.

AI Enrichment

Azure OpenAI lead scoring and next-best-action models integrated into Salesforce workflows.

dbt Transformation Layer

Governed dbt models from staging through marts — compatible with Databricks, Snowflake, or Fabric.

Integration Framework

MuleSoft or Data Factory patterns — reusable for additional Salesforce objects and sources.

IaC Codebase

Terraform — version-controlled, documented, and owned by your team.

Ready to unify your customer data?

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