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Ethos is a data-driven company, and analytics is at the heart of how we make decisions. We are looking for a Staff Data Analyst who will own and drive the data foundations and self-serve analytics strategy for the company. This role will be responsible for designing and maintaining the systems, models, and tools that serve as our single source of truth, while empowering stakeholders to independently access and act on insights. The ideal candidate has a proven track record of building data systems at scale and driving adoption of self-serve analytics. This role requires a rare combination of vision and hands-on execution: you’ll set the direction for how data is modeled, monitored, and consumed — and also roll up your sleeves to build, validate, and ship solutions. You’ll also explore opportunities to bring AI into analytics workflows, enabling faster, more intuitive self-serve access to insights across the business.
Job Responsibility:
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 self-serve 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 their top 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
Requirements:
7+ 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
Hands-on experience with BI/self-serve tools such as Amplitude, Mode, or Looker
Deep understanding of event-based data modeling and instrumentation best practices
Strong experience building monitoring and alerting for data reliability
Ability to translate complex technical concepts into actionable insights for non-technical stakeholders
High bar for data quality and accuracy
detail-oriented with strong validation practices
Proven track record of driving adoption of self-serve analytics across teams
Nice to have:
Experience with AI/ML tools to support analytics workflows is a strong plus