Principal Analytics Engineer, Ceros

• 5+ years of experience in analytics engineering, product analytics, or strategic data roles in SaaS or tech companies
• Proven ability to translate business needs into data models, dashboards, and actionable recommendations
• Expert SQL skills and experience modeling data for BI tools (Metabase, Looker, Mixpanel)
• Strong stakeholder management skills; ability to frame technical work in business terms
• Highly organized with the ability to prioritize multiple initiatives tied to strategic goals

Desirable

• Reporting Into: VP, AI Operations
• We are seeking a Principal Analytics Engineer to drive insight generation, data strategy, and tangible business outcomes across the organization
• This is not a role for an ETL pipeline builder or a manager of data engineers
• Instead, we’re looking for a business-driven problem solver who deeply understands how to frame business questions, identify the right data needed, and deliver actionable insights that inform strategic decisions — for example, "Based on the customer churn data, we should double down on onboarding improvements" or "This product usage pattern suggests new upsell opportunities."
• You will work closely with Product, Sales, Revenue Operations, and Customer Success leaders to translate business problems into data strategies, define critical metrics, and surface insights that lead directly to action
• You'll partner with our Senior Data Engineer and Operations Engineering team to ensure the necessary data is available, but your focus is on analysis, modeling, insight delivery, and business impact
• This role blends 60% strategic insight leadership with 40% hands-on modeling and analysis
• Data Strategy & Business Problem Solving
• Collaborate with cross-functional teams to define key business questions and the data required to answer them
• Structure and model datasets to enable analysis tied directly to business decisions and outcomes
• Lead insight-generation efforts, turning raw data into clear recommendations for action
• Customer and Product Analytics
• Leverage AI/NLP techniques to extract insights from sales calls, support chats, emails, and feedback channels
• Identify patterns in customer behavior, product usage, and support trends to inform product and customer success strategies
• Develop dashboards and reports that move beyond "what happened" to "what we should do."
• Sales & Revenue Operations Insights
• Analyze sales and pipeline data to identify conversion trends, churn risk, and revenue growth opportunities
• Partner with RevOps and Sales leadership to optimize forecasting, segmentation, and sales strategies based on data-driven findings
• Experimentation & Data-Driven Decision Making
• Support A/B testing, growth experiments, and user behavior studies
• Build models that assess the effectiveness of initiatives and recommend next steps based on data
• Enablement & Evangelism
• Develop self-service data tools and training to empower teams to access and interpret insights independently
• Lead workshops and office hours to promote a culture of business-focused data literacy across teams