Salesforce Data Cloud Engineer

Job Summary

• Design and implement unified customer data platforms using Salesforce Data Cloud, focusing on scalable identity resolution pipelines.
• Develop and optimize data pipelines including Data Streams, Data Model Objects (DMOs), and Calculated Insights to support high-quality customer profiles.
• Build audience segmentation and activation workflows for Marketing Cloud, advertising, sales/service, and commerce platforms.
• Define and execute identity resolution strategies (deterministic & probabilistic) to create accurate golden records and improve data completeness.
• Implement data governance and privacy compliance practices, including consent modeling and PII/PHI protection.
• Partner with marketing, product, and CRM teams to translate business requirements into data models, insights, and activation workflows.
• Establish CI/CD processes and automated data quality checks, monitoring, lineage, and SLA reporting for data pipelines.
• Integrate Salesforce Data Cloud with analytics and ML platforms (e.g., Amazon Redshift, Databricks).
• Utilize strong SQL skills and modern data engineering techniques in building and maintaining data infrastructure.
• Work with ETL/ELT pipelines, streaming technologies (Kafka/Kinesis), APIs, and Git-based CI/CD workflows.
• Support customer data activation across multiple platforms, ensuring high data quality and compliance.
• Preferred:
  Experience with Python/dbt, MDM tools, reverse ETL pipelines, Amazon Redshift, and ML operationalization (Einstein Studio/Copado).

• Salesforce Data Cloud
• SQL
• CRM / Customer Data Platforms (CDP)
• Identity Resolution
• Data Pipeline Engineering
• Data Privacy & Consent Management

• 5+ years in data engineering, CRM, or CDP
• 2+ years with Salesforce Data Cloud or similar CDP
• Experience in segmentation, activation workflows, and data governance

This summary provides a concise, list-format overview of the key responsibilities and qualifications for the Salesforce Data Cloud Engineer role at ALSAC.