Data Analyst, User Analytics & Insights

Fetch•Remote,

2d•$92,331 - $108,625•Hybrid

About The Position

As a Data Analyst on the User Analytics & Insights team, you will help cross-functional teams make better decisions through data. This role will support one of two business areas: Marketing or Apps. In either area, you will partner with Product, Marketing, Growth, Engineering, Data, and Business teams to understand user behavior, measure performance, support experimentation, and create reporting that helps teams track progress against key goals. This role is ideal for someone who is highly analytical, comfortable working with large datasets, and able to turn business questions into clear analysis, dashboards, and recommendations. You will be responsible for building and maintaining self-service reporting, creating user segments, supporting experiment setup and analysis, and helping stakeholders understand what is happening, why it is happening, and what actions to take next. This is a full-time role that can be held from one of our US offices or remotely in the United States.

Requirements

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.
Ability 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.

Nice To Haves

Experience with dbt or analytics engineering workflows.
Experience using Git/GitHub for version control, collaboration, or code review.
Experience with Python for data analysis, automation, visualization, or statistical analysis.
Familiarity with martech, lifecycle marketing, campaign platforms, CDPs, attribution, or audience activation tools.
Familiarity with app analytics, event-based tracking, product instrumentation, feature adoption analysis, or app engagement metrics.
Experience partnering with Product, Marketing, Growth, or Engineering teams.
Comfort using Google Sheets for collaboration and lightweight analysis.

Responsibilities

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.
Support decision-making by connecting data insights to user experience, business outcomes, and team priorities.
Document assumptions, definitions, methodologies, and key findings so analysis is reusable and easy to understand.
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.
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.
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.