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Ethos is a life insurance technology company transforming how insurance is bought, sold, and underwritten through a three-sided platform serving consumers, agents, and carriers. The company offers instant, accessible life insurance products with no medical exams, just health questions, eliminating traditional barriers to coverage.
You will own and drive the data foundations and self-serve analytics strategy for the company. This is a rare combination of strategic vision and hands-on execution: you'll set the direction for how data is modeled, monitored, and consumed across the organization, while also rolling up your sleeves to build, validate, and ship solutions.
Key responsibilities include:
**Data Foundations**: Design, build, and validate event-based foundational data models to serve as the single source of truth for funnels, experiments, and core business metrics.
**Self-Serve Enablement**: Build scalable datasets, dashboards, and monitoring systems in tools like Amplitude and Mode to allow teams to independently access and act on insights.
**AI Integration**: Identify and implement AI-driven capabilities to enhance analytics workflows, improve discoverability, and reduce dependency on manual analyst support.
**Reliability & Trust**: Establish monitoring, alerting, and documentation to ensure accuracy, consistency, and reliability of all core metrics.
**End-to-End Ownership**: Scope, design, build, validate, and deploy analytics solutions from raw data pipelines to executive-facing dashboards.
**Adoption & Impact**: Partner with stakeholders to understand use cases, deliver raw datasets and clean metrics, and ensure broad adoption of self-serve tools.
**Vision & Strategy**: Anticipate future needs, identify gaps, and define scalable solutions that raise the bar for the analytics function.
You'll need 8+ years of experience building and scaling data models, pipelines, and self-serve analytics systems. Expert-level SQL and strong experience with DBT or equivalent transformation frameworks are required. Hands-on experience with BI/self-serve tools such as Amplitude, Mode, or Looker is essential. Deep understanding of event-based data modeling, instrumentation best practices, and building monitoring/alerting for data reliability are critical. You should be able to translate complex technical concepts into actionable insights for non-technical stakeholders, with a high bar for data quality and accuracy. Proven track record of driving adoption of self-serve analytics across teams is expected. Experience with AI/ML tools to support analytics workflows is a strong plus.
