Customer Data Analyst

Role overview Build and operate the data foundation used to explain customer adoption, efficiency, and value. This hands-on analytics role sits between customer success, product, data science, and go-to-market operations, translating customer questions into reliable reporting, reusable models, and insights that can inform product improvements.

Responsibilities - Create and tailor customer-facing reports and dashboards, making sure metrics answer customer questions clearly and accurately. - Use SQL to investigate questions, validate reported measures, resolve discrepancies, and trace data quality problems to their source. - Develop reusable data models, transformations, datasets, and metric definitions for reporting, business reviews, and product analytics. - Support customer and executive business reviews by partnering with customer success on data-driven value discussions. - Identify recurring custom requests and work with product and data teams to standardize or productize reporting and improve upstream data capture. - Participate in selected customer meetings or onsite engagements to understand reporting needs firsthand.

Requirements - 2+ years in data analytics, analytics engineering, data operations, business intelligence, customer analytics, or a related technical data role. - Advanced SQL skills, including querying, transforming, validating, and troubleshooting complex datasets. - Understanding of dimensional modeling, data transformations, metric definitions, and analytics architecture. - Knowledge of data quality, validation, governance, and operational workflows. - Ability to turn business and customer questions into structured data requirements and work comfortably with large or complex datasets. - Experience creating dashboards, reports, and executive-level data narratives, with strong communication across technical and nontechnical audiences.

Nice to have B2B SaaS or enterprise software experience; hands-on dbt model development and documentation; customer success, business review, or value realization analytics; familiarity with audit or professional services; product telemetry experience; or collaboration with data science or analytics engineering teams.

Benefits and work setup The source lists flexible paid time off and schedules, retirement savings, wellness support, technology and home-office reimbursement, and compensation that may include ownership.