Senior Customer Success Analytics Analyst

GitLab

4d•Remote

About The Position

As a Senior Customer Success Analytics Analyst, you'll be a key partner to our global Customer Success and Digital CS teams, using data to improve how we engage, retain, and grow our customers at scale. You'll translate questions from Customer Success, Strategy, Marketing, and Product partners into clear analytical requirements. You'll explore customer engagement, adoption, and satisfaction data and turn findings into dashboards, reports, and recommendations that support better decisions across Digital CS programs, scaled engagements, and the broader customer lifecycle. Working closely with Customer Success Analytics teammates and cross-functional stakeholders, you'll build and maintain analytics foundations that power one-to-many engagements, digital touchpoints, and automation. You'll experiment with analytics and customer success tooling to increase speed to insight. You'll also advocate for data quality, consistency, and clear definitions as you shape how we measure and understand customer health and outcomes.

Requirements

Experience in analytics roles focused on customer success and SaaS, including analyzing customer engagement, adoption, and health across the customer lifecycle.
Background combining data from multiple customer and go-to-market systems (for example, customer success platforms such as Gainsight, product analytics, CRM, and marketing automation) to create a unified view of customer behavior and outcomes.
Proficiency writing complex SQL queries with joins, aggregations, common table expressions, and conditional logic to support reporting and in-depth analysis.
Skill in designing and building business intelligence dashboards (such as in Tableau, Looker, or Power BI) that track customer success and digital engagement KPIs, and that are maintainable, scalable, and aligned to stakeholder needs.
Ability to translate complex customer success and digital engagement questions into clear analytical approaches, and to communicate findings and recommendations in a concise, accessible way to both technical and non-technical audiences.
Experience collaborating with cross-functional partners such as Digital Customer Success, Strategy, Marketing, Product, and Sales in a remote, distributed environment.
Attention to data quality, consistency, and performance, with a habit of documenting assumptions, logic, and edge cases clearly.
Openness to experimenting with new tools and methods, including generative AI and experimentation techniques, and to applying transferable skills from related analytics or data roles.

Responsibilities

Partner with Digital Customer Success, Strategy, Marketing, Product, and other Go-To-Market teammates to translate questions about customer health, satisfaction, and engagement into clear analytical requirements.
Design and build dashboards and reports that provide a single source of truth for case management, digital engagement, adoption, retention, conversion, and customer satisfaction metrics.
Explore and analyze data from sources such as customer success platforms, GitLab University, Salesforce, marketing automation tools, and web and product analytics to uncover trends, risks, and opportunities to improve customer outcomes.
Craft well-structured, maintainable visualizations in business intelligence tools that follow internal standards and make it easy for customer-facing teams to monitor performance and take action.
Partner with operational and data teams to define requirements for customer success and engagement data models, shaping how data is collected, structured, and made available for analysis.
Analyze one-to-many engagements and digital touchpoints, including webinars, workshops, emails, and in-app messaging, to understand effectiveness and recommend data-driven optimizations.
Use segmentation, cohort analysis, and experimentation techniques, such as A/B testing, to inform scaled engagement strategies and forecast the impact of digital programs.
Serve as a subject matter expert in customer success analytics by sharing best practices, documenting logic and methodologies, and providing guidance to help partners and other analysts use data to make better decisions.