Salesforce Data Cloud Specialist

Location: PAN INDIA

Required Qualifications:

• 3 to 7 years of experience working with Salesforce platforms (Data Cloud/CDP required).
• Hands-on experience with Data Streams, Data Explorer, Data Bundles, Calculated Insights, Identity Resolution, and Segmentation.
• Strong understanding of data modeling, ETL/ELT processes, and API-based integrations.
• Experience working with Marketing Cloud, Sales/Service Cloud, or Commerce Cloud.
• Familiarity with SQL and large-scale data structures.
• Strong problem-solving, communication, and documentation skills.

Key Responsibilities:

Data Architecture & Strategy

• Architect and implement Salesforce Data Cloud solutions aligned with business goals and data governance standards.
• Define data models, data streams, data bundles, and calculated insights.
• Design customer identity resolution strategies including match rules, identity stitching, and profile unification.

Data Ingestion & Integration

• Build and maintain ingestion pipelines across Marketing Cloud, Sales/Service Cloud, Commerce Cloud, MuleSoft, Snowflake, S3, and other enterprise systems.
• Implement ingestion methods: batch, streaming, APIs, connectors, and data actions.
• Ensure data accuracy, availability, and scalability across all Data Cloud datasets.

Activation & Personalization

• Configure segmentations, triggers, and activation flows across email, advertising, personalization engines, and analytics platforms.
• Enable real-time activation for personalized marketing, sales insights, and customer service experiences.

Governance, Compliance & Security

• Maintain data governance frameworks including consent management, retention policies, and privacy settings.
• Ensure compliance with GDPR, CCPA, and internal data security standards.
• Work closely with Security, Legal, and Compliance teams to ensure safe data handling.

Cross-Functional Collaboration

• Partner with Marketing, Sales, Service, Data Engineering, BI, and Product teams to operationalize unified customer data.
• Provide technical guidance and best practices for Data Cloud usage and scaling.
• Present architecture documentation, data lineage diagrams, and solution recommendations to stakeholders.

Quality Assurance & Optimization

• Monitor Data Cloud performance, ingestion metrics, segment processing times, and activation success rates.
• Troubleshoot data discrepancies, ingestion failures, or identity resolution errors.
• Continually optimize for speed, reliability, and accuracy