Salesforce Data Cloud Engineer

CareCone Group; Sydney; On-site; AUD 110,000 - 170,000; Full time

Job summary

CareCone Group is seeking a data engineer to design and build DMOs/DSOs and mapping layers within Salesforce Data Cloud, and to develop data offload solutions to upstream or downstream platforms.

You will configure identity resolution rules and unified contact profiles, support deployments across Dev to Production, and govern data onboarding with dictionaries, mappings, and validation. Collaboration with DevOps and Analytics teams is essential.

Qualifications

• 3–5+ years of hands-on data engineering experience in a complex enterprise.
• Experience with Salesforce Data Cloud including DMO/DSO development, ingestion, and deployment lifecycle.
• Strong knowledge of Databricks and data lake environments with batch and streaming pipelines.
• Proficiency in SQL and Python for data transformation and pipeline development.
• Experience with multi-environment deployment and change governance.
• Ability to write clear technical documentation.

Responsibilities

• Design and build DMOs/DSOs and mapping layers in SF Data Cloud.
• Develop data offload solutions between Data Cloud and other platforms.
• Configure Identity Resolution rulesets and unified contact profiles in Data Cloud.
• Support deployments across Dev to Production environments with implementation plans and validation.
• Govern data onboarding processes including data dictionaries, mappings, and build validation.

Skills

Data engineering

Salesforce Data Cloud

Databricks

SQL

Python

Data ingestion

Data governance

Tools

Salesforce Data Cloud

Databricks

Job description

• Design and build Data Model Objects (DMOs), Data Stream Objects (DSOs), and source-to-model mapping layers within Salesforce Data Cloud (SFDC360).
• Develop and maintain data offload solutions between the Data Cloud and upstream or downstream platforms.
• Configure and validate Identity Resolution (IR) rulesets and unified contact profiles within the Data Cloud environment.
• Support deployments across development, SIT, UAT, pre-production, and production environments - including preparation of implementation plans, change management documentation, and post-go-live validation.
• Manage and govern data onboarding processes including data dictionaries, attribute mapping reviews, and build validation.

Databricks (Unified Data Platform)

Demonstrate a sound understanding of:

• Ingestion and transformation of data from multiple source domains (e.g. CRM feeds, third-party data, digital signals) into the platform following approved design principles and data governance standards.
• Building and maintaining batch and streaming transform pipelines and workflow orchestration.
• Data dictionary documentation and cloud object storage design for data offload scenarios.
• Collaboration with extended team like DevOps, Data Architects, and operations teams to ensure end-to-end pipeline reliability and observability.
• Demonstrate a sound understanding of how real-time decisioning platforms (such as Pega) consume and depend on data engineering outputs.
• Work with decisioning and analytics teams to ensure accurate and timely data availability for personalisation and customer engagement use cases.
• Participate in cross-team planning and technical reviews as they relate to data dependencies for decisioning workflows.

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Skills & Experience

• 3-5+ years of hands-on data engineering experience in a complex enterprise environment.
• Demonstrated experience with Salesforce Data Cloud - including DMO/DSO development, data ingestion, identity resolution, and end-to-end deployment lifecycle management.
• Strong working knowledge of Databricks, data lake, or similar UDP environment, including batch and streaming ingestion patterns and pipeline orchestration.
• Proficiency in SQL and at least one scripting language (Python preferred) for data transformation and pipeline development.
• Experience working across multiple delivery environments (Dev / SIT / UAT / Pre-Production / Production) with formal change management and release governance processes.
• Ability to write clear technical documentation - implementation plans, data dictionaries, and solution design notes.