Senior Data Engineer / Analyst – Search & AI Visibility

Full-time Description

About Birdeye

Birdeye is the leading agentic marketing platform for multi-location brands.

Companies like H&R Block, Aspen Dental, and Caesars Entertainment use Birdeye to manage marketing across thousands of locations — from how they get found, to how they convert, to how they retain customers. Our platform replaces disconnected point tools with AI agents that execute work at the location level — responding to reviews, updating listings, publishing content, and driving conversions.

Backed by Marc Benioff, Jerry Yang, and Accel-KKR, Birdeye was named to G2’s 2026 Best Agentic AI Products list — appearing alongside the world’s leading AI companies. We’re expanding rapidly into enterprise, with growing adoption across large, multi-location brands.

The Opportunity

This is a hybrid role that combines data engineering, analytics, and product strategy.

You'll build scalable datasets, design data pipelines, analyze massive amounts of search performance data, and partner with Product, Engineering, and Growth teams to identify what improves SEO and Answer Engine Optimization (AEO) performance across our platform.

Rather than building customer-facing features, you'll build the intelligence that helps us decide what features to build next.

Your work will directly influence our product roadmap by helping answer questions like:

• What product capabilities have the greatest impact on organic visibility?
• Why are certain customers or industries outperforming others?
• How do search engine and AI search algorithm changes affect our customers?
• Which product investments create measurable improvements in rankings, traffic, and conversions?
• Where are our biggest opportunities to improve customer outcomes

What You'll Do

Build the Data Foundation

• Design, build, and maintain scalable data pipelines using SQL, Python, Snowflake, dbt, and modern data engineering practices
• Integrate and model data from Google Search Console, Google Business Profile, analytics platforms, ranking providers, AI visibility datasets, and internal product systems
• Ensure data quality, reliability, governance, and auditability across analytical datasets
• Develop reusable data models that support experimentation, reporting, and long-term product analytics
• Continuously improve the performance and scalability of our analytical data infrastructure

Discover What Drives Organic Performance

• Analyze billions of data points to identify the factors that influence SEO and AEO performance
• Investigate underperforming customers, industries, and locations to determine root causes
• Measure the impact of product features on rankings, visibility, traffic, engagement, and conversions
• Evaluate search engine and AI search algorithm updates across customer cohorts
• Build statistical analyses and experiments that separate correlation from causation
• Identify patterns and opportunities that inform future product investments

Turn Data into Product Strategy

• Build executive dashboards and self-service reporting using modern business intelligence and data visualization platforms
• Develop KPIs and measurement frameworks that quantify product impact
• Present clear, actionable recommendations to Product, Engineering, Growth, and executive leadership
• Partner with Product Managers to prioritize roadmap investments based on measurable customer outcomes
• Translate complex analytical findings into recommendations that improve customer visibility and product performance

Collaborate Across the Business

• Serve as the primary analytical partner across Data Engineering, Product, Growth, Customer Success, and Engineering
• Help define how Birdeye measures success across SEO, AEO, and AI visibility
• Establish scalable methodologies for evaluating product effectiveness
• Champion a culture of experimentation and data-driven decision making

AI-Driven Analytics

We expect AI to be part of how you work—not just something you build for others.

Successful candidates are comfortable leveraging modern AI tools throughout the analytics lifecycle to:

• Accelerate SQL, Python, and data engineering workflows
• Explore large and complex datasets more efficiently
• Generate hypotheses and rapidly validate findings
• Automate repetitive analysis, documentation, and reporting tasks
• Improve productivity while maintaining high standards for data quality and analytical rigor

You understand where AI adds value, where human judgment is essential, and how to combine both to deliver better insights faster.