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Revenue Operations Analyst

3 months ago


London, United Kingdom Morpheus Talent Solutions Full time

Revenue Operations Analyst

Location: London


The Challenge

As a Revenue Operations Analyst, you will be responsible for collecting, analyzing, and providing insights from data related to sales and marketing performance, market trends, and customer satisfaction, among other areas. You will be a member of the Revenue Operations team, working closely with the Revenue Operations Lead.


You are a data-driven, customer-centric individual, capable of leveraging a broad array of external tools, interests, and research to propose new solutions for familiar problems. You are genuine, creative, self-motivated, and comfortable operating with ambiguity in a demanding & fast-paced environment.


What You’ll Do

Deliver high-quality analysis, actionable data and insights to increase sales and marketing efficiency

Actively review and provide regular insights on pipeline generation, quality and sales performance

Support the Revenue Operations lead with our operational cadence (e.g. QBRs, Pipeline and Forecast Review) and strategic initiatives

Collaborate across multiple functions within the organization to shape initiatives and drive change

Develop new ideas to shape the strategy and operational rhythm of the division


What’s Needed To Succeed

Bachelor’s Degree required; quantitative subjects (e.g., Mathematics, Economics, Engineering, Finance) preferred

3-5 years of experience in Business/Sales Operations, Strategy, Consulting (or similar) required

Experience in the IT / Software industry and/or B2B Sales preferred but not essential

High analytical proficiency in Microsoft Excel required

Experience with Salesforce.com, BI tools (e.g., Power BI, Tableau) or SQL would be an advantage

Strong business acumen, analytical and interpersonal skills

An ability to communicate clear and concise information to management and cross-functional teams

Strong execution, prioritization and time management skills, whilst dealing effectively with ambiguity