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Fitt

Data Analyst

Posted Yesterday
In-Office
Austin, TX, USA
Mid level
In-Office
Austin, TX, USA
Mid level
Deliver analyses, dashboards, and datasets that inform GTM and Operations. Build and maintain BI dashboards, perform ad hoc analyses, enable self-serve analytics, and collaborate with data/engineering using modern data stack tools.
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Fitt Talent Partners is a specialized recruitment firm working with top health and wellness companies.

We’re filling this role for a client, a leading consumer Health & Wellness Platform — that has existing partnerships with Eight Sleep, WHOOP, Momentous, Peloton, and other top brands.

Job Description

Our GTM and Operations teams rely on rigorous, timely analysis to move fast and make confident decisions — this role is the engine behind that capability. You sit at the intersection of data and decision-making, transforming raw inputs into the dashboards, models, and analyses that keep the business moving.

By driving KPI clarity and enabling self-serve analytics, you reduce friction across the organization and free teams to act on insight rather than hunt for it.

Responsibilities
  • Build and maintain dashboards that give leadership a real-time view of the business: revenue, merchant performance, transaction volume, and customer metrics.

  • Deliver ad hoc analyses for GTM and Operations teams, translating business questions into clear findings and recommendations.

  • Design and maintain datasets and models that empower business teams to self-serve.

  • Use AI tools to move faster, automate repetitive analysis, and focus time on the work that drives the most impact.

Qualifications
  • 3–5+ years of experience in an analytics-focused role.

  • Strong SQL skills: you can independently query, validate, and explore data without engineer support.

  • Strong proficiency with at least one BI tool — Omni, Looker, Metabase, Tableau, or similar — and a track record of building dashboards from scratch that teams use to drive decisions.

  • Familiarity with dbt and modern data stack concepts sufficient to collaborate with data and engineering teams.

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