Archived listing. The details below describe a past opening.
Required Skills & Experience
- 5+ years of Data Engineering experience
- Experience working within Salesforce Data Cloud/Data 360
- Salesforce Data Cloud connector implementation
- Strong Python development experience
- Strong SQL skills
- Experience building and maintaining production data pipelines
- Experience with data transformation and orchestration
- Experience implementing identity resolution logic and customer profile unification
- Experience monitoring and supporting production data environments
- Experience creating data quality checks and automation
- Strong troubleshooting and root-cause analysis capability
- Production support ownership mindset Experience leveraging AI to accelerate engineering work while maintaining accountability for results
Nice to Have Skills & Experience
- Spark experience
- Experience supporting AI, RAG, or vector-search data pipelines
- Agentforce exposure
- Data quality automation experience
- Experience with enterprise CRM ecosystems
- Experience integrating CRM, ERP, Marketing Cloud, SAP, or MES data
- Manufacturing, field service, dealer, channel, or distribution experience
Job Description
The Data 360 Engineer owns the end-to-end movement, transformation, activation, and quality of customer and business data across Data 360. This role serves as the execution layer between data modeling and business activation, building and maintaining pipelines, identity resolution processes, connectors, transformations, segmentation, and data quality automation. The ideal candidate combines strong data engineering fundamentals with CDP experience and production ownership, ensuring trusted unified profiles, reliable pipelines, and high-quality data for analytics, personalization, and AI use cases.
- Build and maintain Data 360 connectors and ingestion pipelines
- Configure and support transforms, segments, and activation workflows
- Implement identity resolution and unified profile logic
- Monitor freshness SLAs and pipeline performance
- Troubleshoot data quality, transformation, and activation issues
- Identify and resolve failures across source systems and downstream consumers
- Build automation for data quality validation and anomaly detection
- Partner with Data Modelers to implement canonical data structures
- Enable AI and Agentforce use cases through quality data foundations and retrieval indexes
- Create and maintain monitoring, alerting, replay, and recovery mechanisms
- Leverage AI to accelerate mappings, transformations, testing, and documentation
- Own production reliability and operational excellence for data pipelines
- Support customer profile activation and business-facing reporting needs