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The Opportunity This is a hybrid role that combines data engineering, analytics, and product strategy. Youll 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, youll 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 Youll 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. What Were Looking For Technical Skills Expert-level SQL with experience analyzing large-scale datasets Strong Python programming skills for analytics, automation, and data engineering Experience designing and maintaining ELT/ETL pipelines Experience working with Snowflake or other modern cloud data warehouses Experience with dbt or similar data transformation frameworks Experience building executive dashboards and self-service reporting using modern business intelligence platforms Strong understanding of data modeling, performance optimization, and analytical best practices Analytical Skills .
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.