Senior AI & Analytics Engineer

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.
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• 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.
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• 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 next AI feature is fast and safe. 

What We're Looking For

• 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.
• Solution Architecture: Experience designing scalable, end-to-end architectures integrating data platforms, services, APIs, AI components, and user workflows.
• 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.
• 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.
• Communication: Ability to explain technical designs and complex concepts clearly to non-technical stakeholders.

Nice To Have

• 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.