Chord Energy

Chord Energy

Division Order Analyst

Houston, TX - Corporate · Senior

Sponsorship not specifiedDetected 12 days ago
ComplianceAccountingExcelResearchCommunicationMicrosoft Office

About the role

  • The ideal candidate will be experienced in handling a wide range of tasks and will be able to work independently with minimal supervision.
  • The position will report to the Division Order Manager and will be located in downtown Houston, TX.
  • Hybrid work schedule is an option for remote work on Mondays and Fridays.

Responsibilities

  • Process all probate information and transfer documents from interest owners in order to maintain a correct division of interest
  • Maintain and correct divisions of interest as payouts and/or recompletions occur

Requirements

  • High School diploma or GED equivalent, or college degree
  • 7 years of experience as a Division Order Analyst
  • Strong knowledge of MS Office, including Word, Excel, and Outlook
  • Ability to balance multiple priorities
  • Physical Requirements and Working Conditions: Must possess the ability to work in a standard office setting and to use standard office equipment, including a computer, copier, files, telephone, and fax
  • The physical demands described here are representative of those that must be met by an employee to successfully perform the essential functions of this job.

Nice to have

  • Bachelor's degree
  • Experience with Enertia Land System
  • Experience working with state and federal leases

Compensation

  • Level and salary commensurate with background and experience.

Equal opportunity

  • Chord Energy does not discriminate in employment on the basis of race, color, religion, sex (including pregnancy and gender identity), national origin, political affiliation, sexual orientation, marital status, disability, genetic information, age, membership in an employee organization, retaliation, parental status, military service, or other non-merit factor.

This listing is sourced directly from Chord Energy's careers page and normalized into a canonical job model.