About Us
Pearl is AI for professional services at global scale, combining advanced AI with verified human expertise to deliver help that is accurate, accountable, and fast. Since 2003, our network has connected millions of customers with licensed professionals across 196 countries, making real expertise available anytime, anywhere.
Our Values
- Data driven: Start with truth, measure what matters.
- Courageous: Bias to action; run toward hard problems.
- Innovative: Seek novel, elegant solutions.
- Lean: Do more with less. Build, ship, learn fast.
- Humble: Strong opinions, lightly held.
About the Role
Analytics is moving beyond dashboards. We're building intelligent systems that let analysts and business users explore data, automate analysis, designing scalable architectures and get to insights faster — and we need a senior engineer to build the data foundations and AI-powered tools that make that possible.
This is a hands-on builder role at the intersection of analytics engineering and applied AI. You'll own the pipelines, datasets, and metrics that the business runs on, and you'll build the LLM-powered applications — copilots, agents, natural-language data experiences — that sit on top of them. You'll work directly with product, growth, and business teams on real B2C and customer data.
What You’ll Do
- Analytics Engineering & Business Analytics
- Build and own scalable data pipelines, transformations, analytical datasets, and reusable data products using SQL and Python.
- Analyze B2C, product, and customer data to identify trends, performance drivers, and actionable insights.
- Translate business and product questions into reliable metrics and decision-support solutions that help teams measure performance and understand customer behavior.
- AI-Powered Analytics
- Design, build, and deploy LLM-powered applications, agents, and analytics workflows using RAG, tool calling, MCP, and modern agent frameworks.
- Develop reusable AI tools that enable analysts and business users to interact with data, automate analysis, and generate insights.
- Evaluate, monitor, and continuously improve AI solutions in production, focusing on accuracy, reliability, cost, and safety.
- Systems Integration & Production
- Integrate data platforms, analytics systems, APIs, and AI components into reliable, secure, and maintainable services.
- Contribute to the architecture and design of scalable data and AI systems in collaboration with platform and engineering teams.
What you'll build
- A governed metrics layer — reliable, reusable definitions for retention, conversion, LTV and other core B2C metrics, built on BigQuery / Fabric with governed definitions in OpenMetadata, that every team and every AI tool draws from.
- An analytics copilot — a natural-language interface that lets product managers and business users ask questions of the data and get trustworthy, sourced answers.
- Automated analysis workflows — agents that handle recurring analyses (weekly performance reviews, anomaly triage, funnel diagnostics) and surface what matters.
- Reusable AI tooling — skills, knowledge sources, documentation, MCP servers, and ready-to-use scripts that AI tools can call for data extraction, analysis, and visualization, so building the
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