Sr Scheduler Application Specialist

Job Title: Sr Scheduler Application Specialist/Sr Data Analyst

Job Location: 100% remote (Cedar Rapids, Iowa, 52401 or Madison Wisconsin)

Duration: 2+ Months (High Chance of extension)

Payrate: $52/hr on W2

We are seeking a detail-oriented Scheduler Application Specialist to support the validation and testing of job scheduling workflows following the migration from Tidal to Control-M. This role is critical in ensuring that all migrated schedules function correctly and reliably in the new Control-M environment. The ideal candidate will have extensive experience in both Control-M and Tidal, with a strong focus on testing, troubleshooting, and validating batch job executions.

Key Responsibilities

• Test and validate migrated job schedules from Tidal to Control-M.

• Collaborate with application and infrastructure teams to identify and resolve discrepancies in job execution.

• Develop and execute test plans and scripts to ensure accurate workflow functionality.

• Monitor job execution and analyze logs to confirm successful completion and error handling.

• Document test results, issues, and resolutions for audit and compliance purposes.

• Support integration testing and user acceptance testing (UAT) for scheduling workflows.

• Provide feedback and recommendations for improving scheduling reliability and performance.

• Assist in troubleshooting and resolving scheduling-related incidents during the migration process.

Required Qualifications

• Bachelor’s degree in Computer Science, Information Systems, or related field.

• 5+ years of experience with Control-M and/or Tidal job scheduling systems.

• Proven experience in testing and validating batch job workflows.

• Strong analytical skills and attention to detail.

• Excellent communication and documentation skills.

• Ability to work independently and manage multiple testing priorities.

Preferred Qualifications

• Experience with Control M and Tidal scheduling tools.

• Knowledge of ITIL practices and change management processes.

• Exposure to cloud environments (AWS, Azure) and hybrid scheduling models.

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