Numeral

Numeral

Data Analyst

HQ - San Francisco, CA

Sponsorship not specifiedDetected 300 days ago
PythonSQLRESTdbtData EngineeringAccountingExcelCustomer SuccessShopifyProblem Solving

About the role

  • ABOUT NUMERAL: Numeral is transforming how taxes get done.
  • Digital businesses are currently bogged down by a painful web of regulations across 47 states and 70+ countries.
  • We're the largest and fastest-growing AI-native tax solution.

Responsibilities

  • Partner with Sales, Engineering, Product, and Solutions teams to troubleshoot and resolve onboarding data issues
  • Support our customer-facing teams with ad hoc data questions, investigations, and insights
  • Build internal tooling and processes to streamline reconciliation and improve onboarding throughput
  • 401(k) to help you build long-term financial security

Requirements

  • Experience with DBT or similar ETL frameworks

Nice to have

  • Proficient in SQL and Excel / Google Sheets
  • Strong analytical and pattern recognition skills across large, messy, or multi-source datasets
  • Experience reconciling financial or transactional data (ideally in an e-commerce or payments environment)
  • Strong problem-solving skills and ability to operate with autonomy in fast-moving environments
  • Clear communicator with high attention to detail and a collaborative mindset

Compensation

  • Competitive salary and equity - you'll share directly in the company's success

Benefits

  • Competitive salary and equity - you'll share directly in the company's success
  • Full medical, dental, and vision coverage
  • Wellness perks like Headspace and the Peloton One App

Company info

  • Numeral is transforming how taxes get done.
  • We're eliminating this burden so teams can focus on their core mission.
  • Started in 2023, Numeral has raised over $57M from Benchmark, Mayfield, Y-Combinator, and many others.
  • We now serve over 3,000 paying customers and have more than tripled our revenue every year in our history.

This listing is sourced directly from Numeral's careers page and normalized into a canonical job model.