Senior AI & Analytics Engineer

About the Role 
The future of analytics isn't dashboards. It's intelligent systems that anticipate questions, surface insights, and help people make better decisions.
 
We're looking for a Senior Analytics Engineer to help build that future at Pearl. In this role, you'll design and develop AI-powered analytics experiences that combine trusted enterprise data with modern AI capabilities, enabling business users to interact with data conversationally and uncover insights faster than ever before.
 
You'll build production AI agents, create scalable semantic data models, develop intelligent analytics applications, and establish best practices for responsible AI across the Analytics organization. Working closely with Product, Engineering, and business leaders, you'll turn emerging AI technologies into real business capabilities that improve decision making across the company.
 
This is an opportunity to help define how AI transforms analytics at Pearl while working on some of the most exciting technologies in data, LLMs, and agentic AI.

What You’ll Do

• Build proactive analytics and AI solutions that surface actionable business insights, anticipate business needs, and enable smarter decision-making.
• Design and develop AI-powered analytics tools, including conversational interfaces that allow business users to query data using natural language.
• Build semantic data models and reusable analytics products that optimize enterprise data for AI applications and large language model reasoning.
• Design, develop, and deploy LLM-powered applications, AI agents, and agentic workflows using modern AI frameworks, Retrieval-Augmented Generation (RAG), and Model Context Protocol (MCP).
• Partner cross-functionally with Product, Engineering, and business stakeholders to identify opportunities where AI agents and intelligent automation can solve real business problems.
• Build scalable enterprise analytics solutions by integrating cloud data platforms, semantic models, and modern AI technologies.
• Develop robust data pipelines, APIs, and backend services using Python and SQL to support production-ready AI and analytics solutions.
• Identify, evaluate, and implement AI, machine learning, and advanced analytics use cases that deliver measurable business value.
• Drive intelligent automation initiatives that improve operational efficiency, reduce manual effort, and streamline business processes.
• Continuously evaluate, test, and optimize AI models, prompts, and intelligent solutions to improve reliability, accuracy, and business outcomes.
• Serve as an Analytics AI Subject Matter Expert (SME), mentoring team members on AI application development, prompt engineering, agent development, AI evaluation, and Responsible AI practices.
• Evaluate emerging AI technologies and recommend opportunities to expand AI capabilities and accelerate innovation across the organization.

What We're Looking For

• Overall 8+ years of experience across Analytics Engineering, Business Intelligence, Data Engineering, AI, or related fields, including at least 5 years of relevant analytics engineering experience.
• Minimum 3 years' experience in designing, developing, and implementing end-to-end AI solutions for analytics functions.
• Strong expertise in business analytics, KPI design, analytical data modelling, and metric governance.
• Advanced programming skills in Python and strong SQL expertise.
• Experience designing scalable data architectures and analytics platforms.
• Hands-on experience building production AI applications using Large Language Models (LLMs).
• Experience with one or more agentic AI frameworks (e.g., LangGraph, Semantic Kernel, CrewAI, AutoGen, OpenAI Agents SDK, or similar).
• Solid understanding of agent orchestration, Retrieval-Augmented Generation (RAG), skills, tool calling, prompt engineering, memory, and AI evaluation.
• Experience integrating AI solutions with enterprise APIs, databases, and cloud platforms (Azure preferred).
• Excellent communication skills with the ability to influence stakeholders and mentor technical teams.

Nice To Have

• Experience building enterprise analytics copilots or conversational analytics platforms.
• Experience with Microsoft Fabric, Azure AI Foundry, Azure OpenAI, Power BI semantic models, Databricks, or Snowflake.
• Knowledge of vector databases, semantic search, and modern AI infrastructure.
• Experience with Docker, Kubernetes, CI/CD, observability, and Responsible AI practices.
• Familiarity with Model Context Protocol (MCP) or similar enterprise AI integration patterns.