Business Insights Analyst, Remote US

Carewell

5d•Remote

About The Position

The Business Insights Analyst helps teams across Carewell understand business performance through actionable reporting and analysis. The role spans e-commerce and service delivery, including financial reporting, marketing efficiency, merchandising and margin, customer cohort behavior, and Care Services productivity. The analyst investigates the drivers behind metric changes, builds reports and dashboards for ongoing stakeholder self-monitoring, identifies useful KPIs, and helps stakeholders determine which business levers to pursue. The role emphasizes measurable business impact, cross-functional adaptability, and analytical craft rather than a path toward people management. Reporting to the Sr. Director of Analytics & Data Engineering, the analyst works within the existing canonical data model and BI architecture rather than owning it. They need to understand table structures and data-model design well enough to direct changes to existing tables that enable analysis, write and maintain complex SQL queries independently, and escalate model or table issues. AI tools such as Claude, Claude Code, and Claude Cowork, along with Hex, are part of the expected everyday workflow from day one. The position is 100% remote. Carewell seeks someone with 3–5 years of relevant analytical experience who can work with stakeholders at all levels, communicate findings in accessible language, and ramp up quickly in unfamiliar business domains.

Requirements

3–5 years of experience in analytics, business intelligence, reporting, or a closely related analytical role.
Demonstrated experience working across multiple business functions, such as marketing, product, finance, or operations.
Strong SQL skills, including independently writing complex analytical queries with joins across multiple tables and aggregations at different levels of grain.
Working understanding of relational table structures and data-modeling concepts, including the ability to read an existing schema and identify changes needed to support new analysis.
Proven experience with modern reporting tools.
Practical, hands-on experience using AI tools to accelerate analytical work and apply them productively from day one.
A specific example from a past role of a report or metric that changed a stakeholder's decision, not merely informed it.
Strong written and verbal communication skills and comfort presenting findings to stakeholders at all levels.
A demonstrated interest in developing analytical craft over the next several years rather than quickly moving toward a management track.

Nice To Haves

E-commerce or direct-to-consumer experience.
Service-business experience, such as support, care, or operations.
Experience with tools such as Hex, Looker, or Omni.
A degree in a quantitative, business, or related field.

Responsibilities

Design and build reports and dashboards that enable stakeholders to self-monitor business performance.
Identify underlying business drivers behind changes in metrics.
Partner with stakeholders to use data in strategic business decisions.
Identify appropriate KPIs for teams and functions and build ongoing reporting to track them.
Build and maintain monthly financial reports, including light analysis of revenue, margin, and costs.
Retire or simplify reports that no longer support decisions.
Analyze marketing efficiency, including CAC, LTV, spend efficiency, and channel-level performance.
Analyze merchandising, revenue, and profit or margin performance at the product and category levels.
Analyze customer cohort behavior, including retention curves, repeat purchases, and Autoship performance.
Analyze Care Services productivity, including throughput, service-level metrics, and staffing efficiency.
Turn stakeholder input into concrete, testable hypotheses about potential business levers.
Build tracking before a lever is pulled, report whether it worked, and adjust the tracking or hypothesis as needed.
Identify broader underlying questions suggested by stakeholder requests.
Work directly with stakeholders at all levels of the organization.
Translate technical and data-heavy findings into clear, actionable language for non-analytical stakeholders.
Understand table structures and data-model design and give direction on re-engineering existing tables to support analysis.
Write complex, efficient SQL queries independently.
Use AI tools and Hex as part of the everyday analytical workflow.
Escalate data-model or table-structure issues rather than repeatedly working around them.