AI Analytics Engineer (AI & Analytics Platform)

Description: Build and maintain context infrastructure such as business glossaries, DBT model enrichment, and semantic layer definitions in Omni Analytics.Design and run evaluation frameworks with test cases, accuracy benchmarks, and validation workflows for AI-generated insights.Build and orchestrate AI agent systems, including prompt pipelines, tool orchestration, query routing logic, and guardrails.Experiment with prompt configurations, agent behaviors, and model outputs to improve accuracy and reliability.Develop internal AI tooling and automations that improve the Data Science & Analytics team's efficiency.Design and develop automated insight generation systems that proactively surface patterns, anomalies, and meaningful business changes.Drive cross-functional adoption by partnering with GTM, Product, Finance, and other teams to onboard users and train stakeholders.Monitor query logs and user behavior to identify gaps in context coverage and opportunities to expand self-service analytics.Triage issues and provide support for users of internal AI-powered analytics tools.Requirements: 1–4 years of professional experience is preferred; 2–4 years in data-related roles is listed in the role profile.Experience in data-related roles such as analytics engineer, data analyst, data scientist, or similar.Strong SQL proficiency.Experience with modern data tools such as dbt, Databricks, Snowflake, or similar.Clear, structured writing that can translate complex business logic into documentation for humans and LLMs.Hands-on experience with AI tools such as Claude, ChatGPT, or Cursor beyond casual use.Cross-functional communication skills for working with non-technical stakeholders.Ability to prototype, pick up new technical skills, and iterate quickly with a builder mindset.Experience partnering with business stakeholders.Experience in SaaS or tech environments is preferred.Experience with BI semantic modeling such as Looker or Omni Analytics is preferred.Familiarity with Python and LLM APIs is preferred.Experience building evaluation or testing frameworks is preferred.Background in context engineering, knowledge management, or technical writing is preferred.Experience with agent architectures, prompt engineering, or AI system design is preferred.Familiarity with data science and ML concepts such as experimentation, time series analysis, statistical modeling, clustering, and anomaly detection is preferred.Benefits: Base salary range of $157,100–$193,600 USD for San Francisco Bay Area, Seattle, New York City, and Los Angeles locations.Base salary range of $141,600–$175,100 USD for all other work locations, including remote.Total compensation may include benefits, restricted stock units, and incentive compensation.Comprehensive benefit offerings.Equal opportunity employer with accommodations available during the application and interview process.