Data Analyst

GitHub, Inc.•UNAVAILABLE, UNAVAILABLE

5d•Remote

About The Position

GitHub is seeking a Data Analyst, Revenue Intelligence to build trusted analytics, data products, and insights across its Revenue, Product, and Engineering organizations. The role turns complex customer, product, usage, and revenue data into scalable analytics and actionable insights, working across business and technical teams. It requires strong analytical and technical skills, exceptional attention to detail, sound business judgment, and the ability to work with multiple stakeholders.

Requirements

At least 5 years of experience in data analysis and reporting, data science, business intelligence, or business and financial analysis; OR a bachelor's degree in Statistics, Mathematics, Analytics, Data Science, Engineering, Computer Science, Business, Economics, or a related field and at least 3 years of relevant experience; OR equivalent experience.
At least 3 years of experience solving real-world business and data problems with code.
At least 3 years of experience with Microsoft Fabric, Power BI, or similar modern data and analytics platforms.
At least 3 years of hands-on experience in data modeling, semantic model development, KPI definition, and data visualization using a tool.

Nice To Haves

Experience with Fabric Apps, Kusto/KQL, Spark, Airflow, or modern cloud data platforms.
Experience building or integrating AI, copilots, agents, or natural-language analytics.
Experience with SaaS, subscription, consumption, or product usage analytics.
Experience with Python and ETL/data pipeline development.
Experience working with Salesforce or other CRM and business systems.

Responsibilities

Develop and maintain key revenue, customer, product usage, and SaaS metrics.
Build analytics experiences using Microsoft Fabric, Power BI, Fabric Apps, and semantic models.
Explore and develop AI-powered and agentic analytics experiences grounded in trusted enterprise data and business definitions.
Create reusable data models, analytical products, and dashboards tied to business and product goals.
Partner with Revenue, Product, Engineering, Finance, and Marketing to define metrics and uncover actionable insights.
Collaborate with Data Engineering and Data Science on ETLs, data pipelines, data quality, and scalable analytical datasets.
Analyze customer behavior, product adoption, sales performance, consumption, and revenue trends to inform strategy.
Communicate complex findings clearly to technical and business stakeholders.