Salesforce Data Engineer || Hybrid in Nashville, Tennessee || USC or GC

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profile Job Location:

Nashville, IN - USA

profile Monthly Salary: Not Disclosed
Posted on: 2 hours ago
Vacancies: 1 Vacancy

Job Summary

Title: Salesforce Data Engineer

Visa: USC/GC

Location: Hybrid in Nashville Tennessee

Client: State of TN

Duration: 7 months

We are looking for an experienced Salesforce Data Engineer based in Nashville Tennessee. -HYBRID (Need Local or someone who can relocate in day 1)


Must have Certification:
Salesforce Platform Developer II
Salesforce Certified MuleSoft Developer
Salesforce Certified Platform Data Architect
Salesforce Certified MuleSoft Platform Architect

Our direct client is seeking a highly-skilled Salesforce Data Engineer to join their team for a critical 7-month project. This hybrid role requires a proven expert in designing building and integrating complex data solutions within the Salesforce ecosystem.

Key Responsibilities:

  • Design develop and maintain scalable data pipelines and integrations between Salesforce and external systems.
  • Architect and implement robust data models data migration and ETL processes.
  • Ensure data quality integrity and security across the platform.
  • Collaborate with stakeholders to translate business requirements into technical solutions.

Ideal Candidate Profile:

  • Extensive hands-on experience as a Salesforce Data Engineer or Architect.
  • Deep expertise in Apex Lightning Web Components and Salesforce APIs.
  • Proven mastery of MuleSoft for building and managing integrations.
  • Strong understanding of data architecture governance and best practices.
  • Excellent communication skills and the ability to work in a hybrid team environment.

Title: Salesforce Data Engineer Visa: USC/GC Location: Hybrid in Nashville Tennessee Client: State of TN Duration: 7 months We are looking for an experienced Salesforce Data Engineer based in Nashville Tennessee. -HYBRID (Need Local or someone who can relocate in day 1) Must...
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Key Skills

  • Apache Hive
  • S3
  • Hadoop
  • Redshift
  • Spark
  • AWS
  • Apache Pig
  • NoSQL
  • Big Data
  • Data Warehouse
  • Kafka
  • Scala