Revenue Data and Analytics Lead

Wood Mackenzie•Chicago, IL

17h

About The Position

Wood Mackenzie is the global leader in analytics, insights and proprietary data across the entire energy and natural resources landscape. For over 50 years our work has guided the decisions of the world’s most influential energy producers, utilities companies, financial institutions and governments. Now, with the world’s energy system more complex and interconnected than ever before, sector-specific views are no longer enough. That’s why we’ve redefined what’s possible with Intelligence Connected. By fusing our unparalleled proprietary data with the sharpest analytical minds, all supercharged by Synoptic AI, we deliver a clear, interconnected view of the entire value chain. Our trusted team of 2,700 experts across 30 countries breaks siloes and connects industries, markets and regions across the globe. This empowers our customers to identify risk sooner, spot opportunities faster and recalibrate strategy with confidence – whether planning days, weeks, months or decades ahead. Wood Mackenzie Intelligence Connected WoodMac.com Wood Mackenzie Brand Video Wood Mackenzie Values Inclusive – we succeed together Trusting – we choose to trust each other Customer committed – we put customers at the heart of our decisions Future Focused – we accelerate change Curious – we turn knowledge into action

Requirements

Experience in revenue operations, business intelligence, or analytics roles, preferably in SaaS/subscription businesses
Technical Skills: Advanced proficiency in Excel/Google Sheets and SQL
Experience with at least one major BI tool
Familiarity with CRM and billing platforms
Understanding of data warehousing concepts and ETL processes
SaaS Metrics Expertise: Deep understanding of SaaS business models and key metrics (ARR, CAC, LTV, churn, expansion, net dollar retention, etc.)
Analytical Mindset: Strong quantitative skills with ability to derive insights from complex datasets
Communication: Excellent ability to present data stories to both technical and non-technical audiences
Partnering: Experience partnering with cross-functional teams including Sales, Customer Success, Product, and Revenue Operations

Responsibilities

Revenue & Customer Analytics Support ARR modelling, forecasting, and variance analysis across the business
Develop and maintain customer account stratification frameworks (by size, industry, engagement, health score, etc.)
Monitor customer activities and engagement patterns to identify trends and opportunities
Analyze drivers of New Logo ARR, Expansion, Contraction, and Churn
Build predictive models for revenue forecasting and customer behavior
Build and maintain KPI metrics including: Annual Recurring Revenue (ARR) Net Revenue Retention (NRR) Gross Revenue Retention (GRR) Customer Acquisition Cost (CAC) and Lifetime Value (LTV) Payback periods and unit economics
Reporting & Data Infrastructure Design and implement dashboards and reporting systems that serve as the single source of truth for business performance
Build and maintain executive dashboards using Power BI and other BI tools
Automate recurring reports and create self-service analytics tools for stakeholders
Ensure data accuracy and consistency across all revenue systems (CRM, billing, product analytics, etc.)
Create board-ready reporting packages for executive leadership
Establish and document data governance standards and best practices
Perform data validation, reconciliation, and anomaly detection across systems
Business Insights & Strategy Translate complex data into actionable insights and strategic recommendations
Highlight growth opportunities, risk areas, and efficiency improvements
Conduct cohort analyses, retention studies, and revenue waterfall analyses
Partner with Sales, Customer Success, Product, and Finance teams to drive data-informed decisions
Systems & Process Management Supporting optimization of end-to-end SaaS reporting
Partner with RevOps and Sales leadership to ensure metric alignment and data accuracy
Develop and maintain documentation for data definitions, metrics, and processes
Continuously improve data collection, processing, and reporting workflows