Salesforce QA Engineer

JR Spain; Bilbao; A distancia; EUR 40.000 - 60.000; Jornada completa

Descripción de la vacante

A technology solutions company is looking for a Salesforce QA Engineer to remotely work from Spain. In this role, you will validate Salesforce infrastructure, perform system integration testing, and create automated test scripts. The ideal candidate has experience in Salesforce platform testing, strong Selenium skills, and proficiency in Python. Good collaboration with DevOps and Data Engineering teams is essential. This position offers a chance to work with scalable systems in a top-tier engineering environment.

Formación

• Proven experience with Salesforce Health Cloud and/or Marketing Cloud QA.
• Strong hands-on skills with Selenium for test automation.
• Experience with Python for API test development.
• Solid understanding of SQL for data validation and extract testing.
• Familiarity with data quality frameworks and production release cycles.

Responsabilidades

• Validate infrastructure and deployment pipelines for Salesforce across environments.
• Perform system integration testing focusing on APIs and performance.
• Create and maintain automated test scripts using Postman, REST-assured, or similar.
• Monitor and report test coverage gaps across infrastructure and services.
• Collaborate closely with DevOps and developers to ensure seamless Salesforce releases.
• Perform smoke, regression, and sanity testing across dynamic environments.

Conocimientos

Salesforce platform testing

Selenium for test automation

Python for API testing

SQL for data validation

Collaboration with DevOps

English proficiency

Herramientas

Jira

Confluence

TestRail

Postman

REST-assured

SoapUI

Descripción del empleo

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? Location: Remote from Spain

Intellias is seeking a Salesforce QA Engineer with strong infrastructure and system-level testing experience. You’ll work on validating not just Salesforce functionality, but the critical underlying architecture, integrations, and DevOps pipelines that support it.

This is an ideal role for someone who has experience with Salesforce platform testing, automation, and environment validation, and wants to work on highly scalable systems with top-tier engineering teams.

• Proven experience with Salesforce Health Cloud and/or Marketing Cloud QA
• Strong hands-on skills with Selenium for test automation
• Experience with Python for API test development
• Solid understanding of SQL for data validation and extract testing
• Familiarity with data quality frameworks and production release cycles
• Ability to work independently while collaborating with DevOps, Data Engineering, and Application Development teams
• Upper-Intermediate or higher level of English for daily team collaboration.

? Responsibilities:

• Validate infrastructure and deployment pipelines for Salesforce across environments
• Perform system integration testing (SIT), focusing on APIs, environment configs, and performance
• Create and maintain automated test scripts (Postman, REST-assured, Selenium, or similar)
• Monitor and report test coverage gaps across infrastructure, APIs, and backend services
• Collaborate closely with DevOps, developers, and QA engineers to ensure seamless Salesforce releases
• Perform smoke, regression, and sanity testing across dynamic environments
• Salesforce platform (admin/dev experience is a plus)
• API testing tools (e.g., REST-assured, SoapUI)
• Jira, Confluence, TestRail

To understand if you are a good match for this role, think if you can fully cover these questions:

1. Can you walk us through a project where you implemented or restored test automation for Salesforce Health Cloud or Marketing Cloud? What tools did you use, and how did you ensure data integrity across systems?

2. How have you used Selenium and Python together in a QA automation context? What challenges did you face when validating data extracts or testing APIs, and how did you overcome them?

3. Describe your experience testing data pipelines involving platforms like Snowflake. How did you collaborate with Data Engineering and DevOps teams to ensure stability and quality in production deployments?