Salesforce Data Cloud Engineer III (Memphis TN or Remote in the USA)

ALSAC is one of the world’s most iconic and respected nonprofits, dedicated to curing childhood cancer. The Salesforce Data Cloud Engineer III provides technical leadership and hands-on engineering expertise in designing and implementing scalable enterprise data solutions, while mentoring team members and ensuring adherence to secure development practices.

Responsibilities:

• Design, develop, and optimize Data Cloud pipelines—including Data Streams, Data Model Objects (DMOs), Data Graphs, Calculated Insights—to build a scalable unified Profile System with high-quality identity resolution and golden records
• Define, tune, and maintain identity resolution rulesets (deterministic and probabilistic) for accuracy, precision/recall, and measurable profile completeness
• Build and optimize Segments and Activation across Marketing Cloud, Advertising platforms, Sales/Service Cloud, and Commerce using privacy-safe matching and deduplication
• Implement data governance, consent modeling, and privacy controls (GDPR/CCPA) across ingestion, unification, storage, and activation
• Partner with Marketing, Product, Analytics, and CRM teams to translate business use cases into Data Cloud data models, insights, and event-driven activations
• Establish and maintain CI/CD pipelines, automated data quality checks, operational monitoring, data lineage visibility, and SLA reporting across ingestion and activation flows
• Collaborate on Lakehouse Federation and Zero-Copy architecture patterns with Amazon Redshift, Databricks, and other enterprise data platforms for analytics, ML integration, and model operationalization (Einstein Studio or external)
• Architect and implement ETL/ELT processes using SQL, streaming platforms (Kafka/Kinesis), APIs, and microservices aligned to enterprise engineering standards
• Analyze and resolve complex technical issues across interconnected systems; perform root cause analysis, implement fixes, and prevent recurrence
• Apply software engineering best practices across front-end, back-end, and data layers while conforming to internal coding standards and secure development practices
• Create, modify, test, debug, document, and maintain complex application programs supporting enterprise systems
• Work directly with users, business analysts, and cross-functional teams to understand requirements, validate user needs, and drive enhancements
• Mentor and support Software Engineer I and II team members through code reviews, technical guidance, and skill development
• Stay current with industry trends, emerging technologies, and Salesforce ecosystem advancements; participate in education, certifications, and learning opportunities
• Maintain thorough documentation (flowcharts, diagrams, layouts, data dictionaries, design specifications) to support system understanding and operational continuity

Requirements:

• 5+ years of experience in data engineering, CRM, or Customer Data Platforms (CDP)
• 2+ years hands-on with Salesforce Data Cloud or an equivalent enterprise CDP
• Bachelor's degree in Information Systems, Engineering, Mathematics, Computer Science, or related field; 5–8 years technical and programming experience
• Strong SQL proficiency and hands-on expertise with Data Cloud entities (DMOs, Data Streams, Data Graphs, Calculated Insights, Segments, Activation)
• Proven experience activating audiences across Marketing Cloud, Advertising platforms, Sales/Service Cloud, or Commerce Cloud
• Experience with ETL/ELT pipelines, streaming technologies (Kafka/Kinesis), REST/GraphQL APIs, and Git-based CI/CD
• Strong understanding of consent management, data privacy, PII/PHI protection, and compliance frameworks (GDPR/CCPA)
• Professional experience developing across multiple layers of the application stack (front-end, back-end, data, APIs)
• Experience in structured, cross-functional environments with established roles and processes
• Experience with Python, dbt, MDM platforms, Reverse ETL, enterprise data warehouses (e.g., Amazon Redshift), Einstein Studio or ML model operationalization, and Copado or similar Salesforce DevOps tooling