Senior Data Analyst – Revenue Intelligence

As a Senior Data Analyst in Kpler’s Business Intelligence team, you will own the revenue intelligence pillar, building and maintaining the data models and reporting that help the business understand how we generate revenue - including ARR, retention, pipeline, churn, and renewals.

You will work with data from Salesforce and subscription billing systems, model it in BigQuery, and serve it through Looker and AI-assisted tools.

This role sits between data engineering and commercial analytics. One week you might be fixing a revenue pipeline or investigating a data quality issue; the next, you’ll be explaining a retention trend to Finance or helping Sales and RevOps understand their data. You’ll have the opportunity to own the numbers that commercial and finance teams rely on to make decisions.

Responsibilities

• Own and maintain governed revenue datasets in BigQuery, powering ARR, GRR, NRR, pipeline, churn, and renewal reporting across Sales, Customer Success, RevOps, and Finance.
• Model data from Salesforce and subscription billing systems into clean, documented reporting tables, with clear data grain, deduplication rules, and metric definitions.
• Build and maintain LookML models, Explores, and dashboards used for revenue reporting, ensuring consistent naming, definitions, documentation, and data quality standards.
• Own analytics delivery end to end, from understanding stakeholder requirements and modelling data through to dashboard development, testing, and documentation.
• Contribute to Kpler’s internal AI revenue analytics tools by curating governed datasets, maintaining reference documentation, and testing outputs against trusted source data.
• Build monitoring and data quality checks to identify issues such as duplicates, mixed-grain fan-out, missing filters, and reconciliation gaps before they impact stakeholders.
• Investigate and resolve revenue data escalations, addressing root causes and improving the underlying data processes to prevent recurring issues.
• Apply software engineering practices to the BI codebase, including version control, peer review, testing, and documentation.
• Work directly with Sales, Customer Success, RevOps, SalesOps, IT, Product, and Finance to turn business questions into clearly defined metrics and analytical deliverables.
• Explain revenue metrics and analytical outputs clearly to non-technical stakeholders, helping teams understand and self-serve their data.
• Use AI tools to accelerate query writing, documentation, analysis, and quality checks, and share effective approaches with the wider team.