Revenue Operations Analyst

About Rystad Energy

Headquartered in Oslo, Norway, and with offices across the globe, Rystad Energy is an independent Energy research and business intelligence company providing data, tools, analytics, and consultancy services to clients exposed to the energy industry across the globe.

Rystad Energy has a range of global databases and research products for companies in the Oil & Gas, Oilfield Services, and Renewables sectors, as well as Investors, Investment Banks and Governments. We also provide a range of advisory services for our clients with capabilities across the Energy value chain. 

Rystad Energy is a rapidly growing company providing the next generation analytical tools and advice for the industry. Rystad Energy combines a high-performance culture with a deeply collaborative ethos, providing ample opportunities for growth, learning, contribution, and career advancement. Read more at www.rystadenergy.com.

The role

As a Revenue Operations Analyst, you will sit at the intersection of business analysis, data analysis and financial analysis. You will turn data from CRM and other business systems into trusted models, commercial insight and practical actions for Sales & Marketing.

Most of the role is hands-on analytical delivery: SQL, Power BI, semantic modelling, validation, automation and controlled AI workflows. A smaller part supports core sales operations, including pipeline hygiene, performance checks and forecasting discipline.

Key responsibilities

• Build and maintain SQL transformations, reusable datasets and analytical models across CRM, commercial, financial and operational data.
• Develop Power BI semantic models, dashboards and reusable DAX measures, with clear relationships and governed calculation logic.
• Translate business questions into structured requirements, trusted KPI definitions and scalable reporting solutions.
• Analyse pipeline, revenue, renewals, activity and usage through trends, variances, performance drivers and gap analysis.
• Validate grain, keys, joins, filters, mappings and deduplication rules, and reconcile reporting outputs to source systems.
• Investigate refresh failures, source changes, data quality issues and reporting discrepancies before they affect business decisions.
• Build QA checks, exception reporting and repeatable workflows that reduce manual effort and improve control.
• Develop and improve approved AI-assisted workflows for analysis, code, documentation and automation, with disciplined human validation.
• Maintain clear metric, lineage and decision documentation, and explain technical findings in practical business language.