Data Analyst, User Analytics & Insights

About the role

The Data Analyst on the User Analytics & Insights team supports one of two business areas: Marketing or Apps. This role involves partnering with Product, Marketing, Growth, Engineering, and Business teams to understand user behavior, measure performance, support experimentation, and create reporting that helps teams track progress against key goals.

Responsibilities

• Data Analysis & Business Partnership
• Partner with Product, Marketing, Growth, Engineering, and Business stakeholders to answer key questions about user behavior, performance, engagement, conversion, and retention.
• Translate business questions into structured analytical plans using SQL, visualization tools, and statistical thinking.
• Conduct routine and ad hoc analyses to identify trends, diagnose metric movements, and surface opportunities for improvement.
• Clearly communicate findings, implications, and recommended next steps to technical and non-technical stakeholders.
• Document assumptions, definitions, methodologies, and key findings so analysis is reusable and easy to understand.
• Dashboarding & Reporting
• Build, maintain, and improve dashboards that track key business, product, marketing, app, and user behavior metrics.
• Create scalable self-service reporting that allows stakeholders to monitor performance and reduce one-off data requests.
• Ensure dashboards are accurate, clearly documented, and easy for cross-functional partners to use.
• Monitor core KPIs and investigate changes in trends, performance, or data quality.
• Partner with Analytics Engineering and Data Engineering to improve data definitions, reporting logic, and source-of-truth data assets.
• Experimentation & Measurement
• Support experiment setup and design, including hypothesis development, metric selection, audience definition, and test-readiness checks.
• Conduct MDE calculations, power analysis, and sample size estimates to help teams understand experiment feasibility.
• Analyze A/B test results, including topline impact, segment-level performance, statistical significance, and follow-up questions.
• Partner with Product, Marketing, and Data Science teams to ensure experiments are measured consistently and interpreted accurately.
• Communicate experiment results clearly to help teams decide whether to launch, iterate, or stop an initiative.
• User Segmentation
• Create and maintain user segments to support analysis, experimentation, lifecycle programs, product experiences, and business reporting.
• Analyze user cohorts based on behavioral, lifecycle, engagement, acquisition, or product usage signals.
• Help stakeholders understand how different user groups behave and where opportunities exist to improve engagement, conversion, retention, or monetization.
• Support audience sizing, segment performance tracking, and readouts for key initiatives.
• For marketing-focused work, support campaign and lifecycle audience creation.
• For apps-focused work, support segmentation related to app usage, feature adoption, engagement, and product funnels.

Qualifications

• 2+ years of experience in analytics, data analysis, product analytics, marketing analytics, business analytics, or a related field.
• Strong proficiency in SQL for data manipulation, analysis, validation, and reporting.
• Experience building dashboards and visualizations in tools such as Hex, Tableau, Looker, Grafana, Mode, or similar platforms.
• Working knowledge of A/B testing concepts, including hypothesis creation, metric selection, sample sizing, segmentation, and result interpretation.
• Experience creating user segments, cohorts, or audience groups for analysis, experimentation, reporting, or targeting.
• Ability to analyze funnels, retention, engagement, conversion, campaign performance, app behavior, or other user/business metrics.
• Strong attention to detail and ability to identify data quality issues.
• Strong communication skills, with the ability to explain findings clearly to both technical and non-technical audiences.
• Able to manage routine analytical requests independently while escalating ambiguity, blockers, or trade-offs with clear context.
• Comfort working in a fast-paced environment with evolving priorities and imperfect data.