Salesforce Data Cloud Engineer – Audience & Activation

Salesforce Data Cloud Engineer – Audience & Activation

Summary

Responsibilities

• Design, implement, and optimize Calculated Insights and Computed Traits to enhance audience segmentation and targeting capabilities.
  
• Develop and manage complex SQL queries to extract, transform, and load data efficiently within the Salesforce ecosystem.
  
• Configure and maintain Data Extensions to support scalable data storage and retrieval operations.
  
• Architect data pipelines and integrations utilizing cloud infrastructure such as Snowflake, AWS, Azure, or GCP.
  
• Conduct thorough data quality assessments and troubleshooting to resolve discrepancies and ensure data integrity.
  
• Establish and enforce Consent management protocols and Data Governance frameworks to maintain regulatory compliance.
  
• Manage the configuration of Salesforce Data Cloud (Data 360) to support real-time data ingestion and processing.
  
• Implement and monitor Data Streams to facilitate continuous data flow between source systems and the data cloud.
  
• Define and maintain Data Model Objects (DMOs) to structure data relationships and support advanced analytics.
  

Requirements

• Minimum of 4+ years of professional experience in data engineering, cloud architecture, or related technical fields.
  
• Proven expertise in Salesforce Data Cloud, specifically regarding Data 360 and its core components.
  
• Strong proficiency in SQL for data manipulation, analysis, and reporting.
  
• Hands-on experience with cloud platforms including Snowflake, AWS, Azure, or Google Cloud Platform (GCP).
  
• Demonstrated ability to manage Data Extensions and Data Model Objects (DMOs) effectively.
  
• Deep understanding of data quality principles, troubleshooting methodologies, and governance standards.
  
• Experience working with Calculated Insights and Computed Traits to derive meaningful business intelligence.
  
• Knowledge of Consent management practices and data privacy regulations.
  
• Familiarity with Data Streams and real-time data processing architectures.
  
• Ability to translate complex data requirements into technical solutions that align with business goals.