Salesforce Data Cloud Engineer

NAM Info Inc; Hyderabad; Hybrid; INR 1,200,000 - 1,800,000; Full time

Job summary

NAM Info Inc is seeking a Salesforce Data Cloud Engineer to design, implement, and maintain robust data integration pipelines across enterprise systems and Salesforce Data Cloud. You will enable 360° customer views and real-time personalization by handling ingestion, identity resolution, unification, activation, and insights.

Join a cross-functional team and work in a hybrid model with a focus on scalable data pipelines, data governance, and CI/CD for Data Cloud configurations using Copado,

Qualifications

• Experience with Salesforce Data Cloud (Genie/CDP) required.
• Strong knowledge of Data Streams, DMOs, unified profiles, and insights.
• Experience connecting Data Cloud with SFMC, Sales Cloud, and external analytics.
• Familiarity with Salesforce APIs (REST, Bulk, Composite) and SF product stack.

Responsibilities

• Design scalable data ingestion pipelines into Salesforce Data Cloud from multiple systems.
• Configure Data Streams, DMOs, and Calculated Insights for 360° views.
• Ensure identity resolution and data unification across domains.
• Integrate Data Cloud with SFMC, Sales Cloud, and external analytics tools.
• Apply data hygiene, governance, and quality best practices.
• Build and monitor CI/CD pipelines for Data Cloud using Copado, GitLab or Gearset.
• Develop environments management (Dev/UAT/Prod) using scratch orgs or sandboxes.

Skills

Salesforce Data Cloud

Data ingestion

Identity resolution

Data unification

Data quality

CI/CD pipelines

Tools

Copado

GitLab

Gearset

Apache Airflow

GCP Data Pipelines

SQL

REST APIs

Job description

Job Title: Salesforce Data Cloud Engineer

Work Preference: Hybrid

Notice Period: Immediate to 15 days

Mandatory Skills: Salesforce Data Cloud

Shift Timing/On-Call/Weekend Support Needed- Second shit: 2.00 PM to 10.00 PM IST

How many rounds of Interview: 2 LEVELS

Employment Type: Full-time

Exp: 5+ Years

Role Summary:

We are seeking a highly skilled Salesforce Data Cloud Engineer to design, implement, and maintain robust data integration pipelines between enterprise systems and Salesforce Data Cloud. This role involves working across data ingestion, identity resolution, unification, activation, and insights, enabling 360° customer views and real-time personalization.

Key Responsibilities:

• Design and implement scalable data ingestion pipelines into Salesforce Data Cloud from internal and external systems.
• Configure and manage Data Streams, Data Model Objects (DMOs), and Calculated Insights.
• Work with cross-functional teams (marketing, CRM, analytics) to ensure accurate customer identity resolution and data unification.
• Integrate Salesforce Data Cloud with Salesforce Marketing Cloud, Sales Cloud, and external analytics platforms.
• Implement and manage data hygiene, governance, and data quality best practices.
• Build and monitor CI/CD pipelines for Data Cloud configurations using tools like Copado, GitLab, or Gearset.
• Develop scalable processes for managing environments (Dev, UAT, Prod) using scratch orgs or sandboxes.

Required Skills & Qualifications:

• Experience working with Salesforce Data Cloud (Genie/CDP).
• Deep understanding of Data Streams, Data Model Objects (DMOs), Unified Profiles, Calculated Insights, Segments.
• Experience configuring Data Cloud Connector for SFMC, and activating data to Marketing Cloud.
• Familiarity with Salesforce Marketing Cloud, Sales Cloud, or Service Cloud.
• Familiarity with Salesforce APIs (REST, Bulk, Composite).

Good to have

• Experience with ETL/ELT pipelines using tools like Apache Airflow, GCP Data Pipelines.
• SQL and data modeling skills.
• Understanding of JSON, Parquet, Avro, and large-scale data processing.
• Familiarity with GCP
• Experience with CI/CD pipelines, especially using Copado, GitLab.

Soft Skills:

• Strong problem-solving ability and attention to detail.
• Ability to work in a fast-paced, agile environment.
• Excellent communication skills to work with stakeholders across engineering, marketing, and analytics teams.