Revenue Ops Analyst: Data-Driven Growth & Insights

Rystad Energy

Kuala Lumpur

Hybrid

MYR 60,000 - 100,000

Full time

7 days ago

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Job summary

Rystad Energy is seeking a Revenue Operations Analyst to turn CRM and other business data into trusted models and actionable insights for Sales & Marketing. You will work at the intersection of business, data, and financial analysis.

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

Qualifications

3+ years of experience in sales operations, business/data analysis, BI or analytics.
Bachelor’s degree in Data Analytics, Computer Science, Information Systems, Software Engineering or related field preferred.
Strong SQL skills and practical experience with Power BI, DAX, Power Query and advanced Excel.
Solid understanding of data modelling concepts (facts, dimensions, grain, keys, relationships).
Proven ability to build reliable reporting datasets and reconciliations across data layers.
Commercial and financial acumen with ability to understand performance drivers and forecasts.
Strong business analysis skills to structure problems and define requirements.
Detail-oriented with disciplined validation, debugging and documentation.
Ability to work independently and build relationships with global stakeholders.
Experience with CRM systems and data across commercial/finance/product is advantageous.
Exposure to Python, Git, APIs, JSON, Microsoft Fabric, Tabular Editor or DAX Studio is advantageous.
Responsible use of AI tools with sound judgment around accuracy and data access.

Responsibilities

Build and maintain SQL transformations and analytical models across CRM, financial and operational data.
Develop Power BI semantic models, dashboards and reusable DAX measures with clear relationships.
Translate business questions into structured requirements and KPI definitions for scalable reporting.
Analyze pipeline, revenue, renewals, activity and usage through trends and gaps.
Validate grain, keys, joins, filters, mappings and deduplication rules; reconcile outputs to sources.
Investigate refresh failures, data quality issues and reporting discrepancies.
Build QA checks, exception reporting and repeatable AI-assisted workflows.
Develop and improve AI-assisted workflows with disciplined human validation.
Maintain clear metric lineage and explain findings in practical business language.
Support core sales operations checks for pipeline hygiene, funnel movement and forecast performance.