Pearl - Senior AI & Analytics Engineer
Requirements
• Relevant Experience: 6+ years of hands-on experience in analytics engineering, data engineering, product/business analytics, applied AI, or a related technical role. • SQL & Python: Strong proficiency in SQL and Python, with experience building and maintaining production-grade data pipelines and analytical datasets beyond exploratory notebooks. • SQL & Python • Solution Architecture: Experience designing scalable, end-to-end architectures integrating data platforms, services, APIs, AI components, and user workflows. • Solution Architecture: • Business & Product Analytics: Experience with B2C, product, or customer analytics, including defining and applying metrics such as retention, funnels, cohorts, conversion, and customer lifetime value (LTV) to influence business decisions. • Business & Product Analytics: • Production AI Experience: Demonstrated experience personally building and deploying at least one LLM-powered application used by real users, leveraging RAG, tool/function calling, or agents. • Agent Frameworks: Hands-on experience with at least one orchestration or agent framework, such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, or similar. • Agent Frameworks: • Communication: Ability to explain technical designs and complex concepts clearly to non-technical stakeholders. • Communication: • Experience with AI evaluation, monitoring, guardrails, or Responsible AI practices in production. • Built conversational analytics, an analytics copilot, or text-to-SQL / natural-language data experiences. • Worked with semantic layers, governed metrics, or enterprise metric definitions. • Google BigQuery, Google Analytics, OpenMetadata, Microsoft Fabric, Power BI, Databricks, Snowflake, Azure AI Foundry, Azure OpenAI, or Claude. • Vector databases, semantic search, or MCP server development. • Docker, Kubernetes, CI/CD, or observability tooling. • Our Commitment to an Inclusive Workplace
Responsibilities
• 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 • 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. • A governed metrics layer • An analytics copilot — a natural-language interface that lets product managers and business users ask questions of the data and get trustworthy, sourced answers. • An analytics copilot • Automated analysis workflows — agents that handle recurring analyses (weekly performance reviews, anomaly triage, funnel diagnostics) and surface what matters. • Automated analysis workflows • 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 next AI feature is fast and safe. • Reusable AI tooling
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