Data Analyst

Overview: The Data Analyst will join a team of 80+ purpose-driven staff members in a friendly, focused, fast-paced entrepreneurial environment. The National Society of Leadership and Success (NSLS) is the largest accredited leadership honor society in the United States, with over 800 chapters and more than 2 million members. The Data Analyst will join a team of 80+ purpose-driven staff members in a friendly, focused, fast-paced entrepreneurial environment. The National Society of Leadership and Success (NSLS) is the largest accredited leadership honor society in the United States, with over 800 chapters and more than 2 million members. The Data Analyst will join a growing data team and partner closely with our stakeholders to translate business questions into actionable insights. You'll embed yourself in marketing, finance, and sales to understand how the business operates, identify the right metrics and dimensions, and build visualizations that help stakeholders make faster, smarter decisions. A key early focus will be supporting our Redshift-to-Snowflake migration — auditing legacy dashboards, QA-ing reports, and validating data accuracy in the new environment. Responsibilities: In Your First 6 Months Support the Snowflake migration: Audit and QA legacy Redshift dashboards and reports, validate logic against new dbt models, and help ensure a clean cutover Build business-facing dashboards: Develop intuitive, self-service reports in Hex that give marketing, finance, and sales teams the visibility they need Establish metric definitions: Partner with stakeholders to translate business language into well-defined metrics and dimensions, then document them for shared understanding Collaborate with the data team: Work in a sprint-based workflow with our Data Engineer, Analytics Engineer, BI & Automation Lead, and Head of Data Ongoing Responsibilities Write and maintain SQL and dbt models: Develop clean, well-documented transformations that power gold-layer business reporting Embed with business teams: Proactively identify the questions being asked by marketing, finance, and sales — and bring data products to them before they have to ask Democratize data access: Build dashboards and self-service tooling that reduce ad hoc requests and empower stakeholders to find answers independently Tell stories through data: Communicate findings in a way that is clear, compelling, and actionable for both technical and non-technical audiences Run analyses and experiments: Design and interpret A/B tests, build forecasting models, and apply statistical methods (regression, cohort analysis, confidence intervals) to support business decisions Use AI to work smarter: Leverage AI tools (Claude, Copilot, Cursor, or similar) to accelerate analysis, automate repetitive tasks, and expand what's possible Qualifications: 2–3 years of experience as a Data Analyst, Analytics Engineer, or similar role Strong SQL skills: You write clean, advanced SQL including CTEs, window functions, and optimized queries that others can read and maintain dbt proficiency: You've worked with dbt in a production environment and understand modeling best practices Visualization experience: You've built dashboards and reports in Hex, Looker, Tableau, or a comparable BI tool — and you know how to design for clarity, not just completeness Stakeholder communication: You can bridge the gap between technical and non-technical audiences, explain your methodology, and present findings with confidence Statistical fundamentals: You understand hypothesis testing, A/B test design, regression analysis, and confidence intervals, and can apply them correctly Forecasting experience: You've built or contributed to forecasting models using cohort trends, maturity curves, or regression techniques AI-assisted development: Proficient with AI tools to accelerate work, debug queries, and learn new technologies quickly Nice To Haves: Experience with Snowflake as a data warehouse Python fundamentals (pandas, notebooks) for data wrangling or analysis Background in marketing, finance, or sales analytics Familiarity with HubSpot or similar CRM data Who You Are: A storyteller: You don't just pull numbers, you craft narratives that make data actionable for the people who need it Proactively curious: You surface questions stakeholders should be asking and bring answers before they're requested A bridge-builder: You're equally comfortable whiteboarding with a VP as you are writing a dbt model, and you translate between both worlds fluently Quality-focused: You document your work, write readable SQL, and leave the codebase better than you found it AI-native: You use AI tools as a genuine force multiplier while maintaining rigor and ownership over your output Mission-driven: You want your work to matter: to students, to the organization, and beyond a dashboard How We Work: Sprint-based workflow: Bi-weekly sprints with planning, standups twice per week, and regular retrospectives Weekly 1:1s: Regular check-ins with the Head of Data for feedback, support, and career growth Collaborative code review: Your SQL and dbt models will be reviewed and you'll review others' Work-life balance: Standard business hours (9-5 or similar), no on-call or off-hours expectations AI-assisted development: We actively encourage the use of AI tools to write better analyses faster and increase overall team velocity Tech Stack: Data Warehouse: Snowflake (migrating from Redshift) Transformation: dbt Cloud Visualization: Hex Customer Data Platform: PostHog Infrastructure: AWS Source Systems: HubSpot, Drupal, Symfony, Shopify (100+ tables, <1TB total) The National Society of Leadership and Success is an equal opportunity employer committed to diversity, equality, and inclusion Visit nsls.org to learn more about our organization