Manager of Analytics & Strategy

About the role

At Read AI we're redefining how teams collaborate by bringing intelligence to every conversation. Our platform supercharges productivity across meetings, messages, and email, so work gets done faster, smarter, and with better focus. We integrate seamlessly with tools like Zoom, Microsoft Teams, Google Meet, Slack, and more, helping teams stay aligned and move forward, whether they're in the same room or across time zones. Backed by $81 million in funding from Smash Capital, Madrona, and Goodwater Capital, Read AI is growing. If you're excited to shape the future of AI-powered collaboration and want to make an impact at a product-focused startup, we'd love to meet you. We are seeking a Manager of Analytics and Strategy to own trusted product reporting, metric governance, and the semantic layer that powers business decision-making. This role is highly hands-on at the start, leading as an individual contributor who interprets product and customer data to surface clear insights, diagnose performance shifts, and recommend actions that drive adoption, retention, and revenue. Over time, this leader will build and develop a product analytics function, establishing the operating cadence, standards, and capabilities needed to scale consistent measurement, data integrity, and decision support across the company.

Responsibilities

• Product Reporting and Insights
• Partner with Product and cross-functional leaders to define core product metrics/KPIs and measurement strategy (activation, engagement, retention, conversion, expansion).
• Build and maintain dashboards that surface trends, anomalies, and leading indicators; ensure metric definitions and instrumentation remain accurate and consistent.
• Investigate performance changes, identify root causes, and communicate implications, tradeoffs, and actionable recommendations to inform product strategy and roadmap.
• Data Integrity and Metric Governance
• Own metric definitions and governance to ensure consistency across teams and reporting.
• Implement data quality checks and monitoring for key datasets and KPIs.
• Document metric logic, business rules, and data lineage for core reporting models.
• Identify data gaps and instrumentation needs and drive resolution with cross-functional partners.
• Semantic Layer Ownership and Engineering Partnership
• Own the semantic layer that joins data, applies business logic, and produces curated datasets and metrics for analysis and dashboards.
• Build and maintain analytics-ready data models that enable scalable insights and self-serve reporting (with the support of other Sr. Data Analyst)
• Align with Engineering on data contracts, table availability, freshness expectations, and quality standards.
• Partner with Engineering on a clear data ownership model, where Engineering is responsible for the pipelines and infrastructure that move raw data into the data lake and warehouse.
• Cross-Functional Decision Support
• Partner with product, marketing, lifecycle, sales, and leadership to translate insights into actions and priorities.
• Support segmentation, measurement plans, and experiment evaluation.
• Surface churn risk and expansion signals using usage patterns and lightweight scoring approaches.
• Deliver executive-ready readouts that connect product performance to decisions, tradeoffs, and next steps.
• Required Qualifications:
• 8+ years of experience in product analytics, business intelligence, or a related analytics and insights role.
• Proven experience owning product performance reporting and translating trends into business actions.
• Strong background working with product usage data tied to retention and revenue outcomes.
• Proficiency in SQL and Excel; experience using Databricks and BI tools.
• Demonstrated ownership of data quality, metric governance, and trusted reporting layers.
• Strong communicator able to synthesize complex data into clear narratives and recommendations.
• Comfortable operating in a fast-paced, cross-functional environment.

Requirements

• Experience owning a semantic layer or analytics modeling layer (for example dbt, LookML, or metric layers).
• Experience with experimentation measurement, cohort analysis, or lifecycle analytics.
• Familiarity with lightweight scoring approaches for churn risk and expansion readiness.
• Why Read AI?
• We've also introduced our new desktop apps for Windows and macOS and our Android app, joining Read AI for iPhone and web.
• Massive Impact: AI's greatest impact will be on the ability to allow people to do more, taking away mundane tasks, and g

Your Match

How well this role fits your profile.

Company Intel

What employees say

Worked at read-ai? Share your experience