Senior Finance Analytics Manager, Grafana Labs

• Professional Experience: 5+ years of experience in corporate analytics, financial analytics, data science, or a directly related quantitative field. Experience in a fast-paced technology or SaaS business is highly preferred
• SQL Mastery: 5+ years of hands-on SQL experience, with a daily habit of writing complex, highly efficient queries to mine data and build production-ready data models
• Data Visualization: 3+ years of experience building user-focused, production-quality dashboards in tools like Looker, Tableau, Mode, or Sisense. Previous experience with Grafana visualization, or a strong desire to invest the time to learn it, is a major plus
• Data Stack & Architecture: Practical experience performing data model development, building ETL processes, and working within modern data warehouses (e.g., BigQuery, Snowflake). Familiarity with dbt is highly desirable
• Business Systems: Familiarity with core enterprise systems and data structures, specifically NetSuite and Salesforce, to understand how financial and operational data flows from the source to reporting layers
• Executive Communication: Exceptional communication skills with a proven track record of translating complex analysis into structured narratives for senior management. You can seamlessly explain "what the data is," "why it matters," and "what we should do about it."
• Analytical Balance: Strong critical reasoning and financial modeling skills. You understand the common pitfalls of data analysis, know how to track core SaaS metrics (ARR, NDR, retention), and can map operational trends directly to financial outcomes

Desirable

• We are looking for a Senior Finance Analytics Manager to join our Finance and Strategy team and stand up our Corporate Finance Analytics function
• This is a high-impact, individual contributor role with end-to-end ownership and no direct people management responsibilities, reporting to the Senior Finance Director of Corporate, R&D, and G&A
• To be successful, you should look forward to operating in an unstructured, fast-paced, remote-first environment where you will establish the reporting guardrails and analytical frameworks for our future growth, bringing greater predictability and insight to support high-tempo, data-driven decisions
• In this role, you will gain deep exposure to company-wide key operating metrics (e.g., Customer Count, ARR, NDR) and provide critical insights to strategic decision-making
• You will help to define the building blocks of a sound data and financial analytics practice, developing impactful analyses, building datasets, and creating dashboards for our evolving business
• This role requires a unique analytical balance: you have the solid technical foundation to put a complex data problem under a microscope, combined with the business judgment, resourcefulness, and executive-level communication skills needed to see the bigger picture
• Data Modeling & Architecture: Partner with distributed data teams to build, iterate, and optimize our corporate data architecture. You will develop reliable data models to help define company-wide performance metrics, tracking them seamlessly from raw source data down through user-friendly reporting layers
• Dashboards & Reporting: Design, build, and maintain production-quality dashboards and scalable data products. You will establish reliable, automated tooling that allows internal stakeholders to monitor key operating metrics in real-time
• Advanced Analytics & Deep Dives: Build advanced analytical models to unlock deeper insights into customer behaviors and macro trends, mapping operational opportunities directly to financial outcomes
• Executive Storytelling: Synthesize complex data and analyses into compelling, structured narratives and actionable insights for executive consumption and senior leadership decision-making
• Enablement & Governance: Lead cross-functional projects to develop datasets that support the organization's evolving needs; provide documentation and continuous enablement to help finance and business teams self-serve basic data needs
• Operational Excellence: Set a high bar for analytics integrity across the organization by managing urgent, ad-hoc data analysis requests and maintaining rigorous QA support for core financial data