🎁 Before you apply, rehearse this interview. Create your free WorkMundi account and get an Interview Training on HelpsYouSpeak — no cost, no card. I want my training →
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 G2s 2026 Best Agentic AI Products list appearing alongside the worlds leading AI companies. Were 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 practicesIntegrate and model data from Google Search Console, Google Business Profile, analytics platforms, ranking providers, AI visibility datasets, and internal product systemsEnsure data quality, reliability, governance, and auditability across analytical datasetsDevelop reusable data models that support experimentation, reporting, and long-term product analyticsContinuously 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 performanceInvestigate underperforming customers, industries, and locations to determine root causesMeasure the impact of product features on rankings, visibility, traffic, engagement, and conversionsEvaluate search engine and AI search algorithm updates across customer cohortsBuild statistical analyses and experiments that separate correlation from causationIdentify 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 platformsDevelop KPIs and measurement frameworks that quantify product impactPresent clear, actionable recommendations to Product, Engineering, Growth, and executive leadershipPartner with Product Managers to prioritize roadmap investments based on measurable customer outcomesTranslate 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 EngineeringHelp define how Birdeye measures success across SEO, AEO, and AI visibilityEstablish scalable methodologies for evaluating product effectivenessChampion a culture of experimentation and data-driven decision making AI-Driven Analytics We expect AI to be part of how you worknot 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 workflowsExplore large and complex datasets more efficientlyGenerate hypotheses and rapidly validate findingsAutomate repetitive analysis, documentation, and reporting tasksImprove 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. Technical Skills What We're Looking For Expert-level SQL with experience analyzing large-scale datasetsStrong Python programming skills for analytics, automation, and data engineeringExperience designing and maintaining ELT/ETL pipelinesExperience working with Snowflake or other modern cloud data warehousesExperience with dbt or similar data transformation frameworksExperience building executive dashboards and self-service reporting using modern business intelligence platformsStrong understanding of data modeling, performance .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.