Salesforce Data Cloud Engineer | Real‑Time Pipelines

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

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.