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Workday Integration Engineer

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Onsite

Workday Integration Engineer

scudl

Workday Integration Engineer

Skills

Data AnalysisAIWorkday

5 days ago

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Important information


Contract type:

Freelance / Contract

Work mode:

Onsite

Published on:

15 October 2026

What they need


Workday Integration Engineer

Responsible for designing, building, supporting, and optimising Workday integrations across HR, Finance, and third-party platforms. Develop and maintain integrations using Workday Studio, EIB, Core Connectors, APIs, and Workday web services to ensure secure, reliable, and scalable data flows. Partner with business and technical stakeholders to troubleshoot issues, improve automation, and deliver integration solutions that support operational excellence and enterprise transformation initiatives.

  • Tertiary qualifications in the software engineering discipline
  • Experience with Workday would be highly beneficial, as this position will lead the technical data stream
  • At least 4 years’ experience in a similar role
  • Superannuation/Wealth management/financial services knowledge
  • Strong technical knowledge of computer systems, features and integration capabilities
  • Strong analytical and problem-solving capabilities
  • Strong communication both verbal and written with the ability to interpret customer business needs and translate into application and operational requirements
  • Strong documentation writing skills
  • Strong interpersonal skills with the ability to effectively collaborate and manage expectations.
  • Demonstrated ability to lead data strategy, governance, and operating practices that enable responsible AI and advanced analytics in a regulated environment.
  • Strong understanding of how data quality, metadata, lineage, semantic consistency, access control, and stewardship enable trusted AI outcomes.
  • Ability to identify and shape practical AI opportunities from enterprise data, translating business problems into governed, measurable, and ethically sound data-enabled solutions.
  • Working knowledge of responsible AI principles, including explainability and human oversight,

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