Principal Analytics Engineer

Elastic, the Search AI Company, enables everyone to find the answers they need in real time using all their data at scale. They are seeking a Principal Analytics Engineer to lead the design and build of an AI-powered intelligence system that synthesizes complex data streams into a unified, high-fidelity system for the entire customer journey.

Responsibilities:

• Architect the Foundation: Design and build the core BigQuery and dbt infrastructure that powers Elastic’s marketing intelligence, transforming raw signals into high-fidelity, agent-ready data products
• Enable AI & Agents: Develop the semantic layer and structured knowledge base that allows AI agents to accurately "talk" to our business data and reason through complex performance questions
• Map the Journey: Integrate disparate signals across digital, product, and sales into a unified lifecycle model that tracks the customer’s path from discovery to revenue
• Scale through Partnerships: Partner with Enterprise, Product, Sales, and Finance teams to align on shared metrics while mentoring other engineers to uphold high standards for our data foundation

Requirements:

• Data-as-a-Product: You treat data as a high-value product. You are dedicated to the user experience of data—ensuring it is discoverable and reliable for both human teammates and AI agents
• Technical Proficiency: Deep experience with BigQuery, dbt, and semantic layers (e.g., dbt Semantic Layer, Vortex AI). You have a proven ability to apply automation or LLM-assisted workflows to the data modeling lifecycle
• Architectural Design: Ability to build complex, interconnected systems by starting with the desired outcome and working backward. You enjoy creating extensible frameworks that empower others to innovate
• Systems & Design Thinking: The ability to look at a complex web of data and see the underlying architecture required to make it simple and extensible
• Collaborative Communication: A track record of 'translating' technical debt into business value and coaching peers through complex architectural hurdles
• Operational Excellence & Governance: You treat data as infrastructure. You have deep experience implementing data contracts, automated quality monitoring (DQM), and governance frameworks that ensure metrics remain consistent, secure, and reliable across the enterprise
• GTM Fluency: A strong understanding of Go-To-Market mechanics—knowing how technical data structures translate into business-critical concepts like customer acquisition, attribution, and revenue
• Marketing Science Foundations: Familiarity with Marketing Mix Modeling (MMM), causality, or incrementality analysis to help the business understand the true ROI of different channels
• Privacy & Ethics: Understanding of GDPR/CCPA compliance and how to manage data privacy and consent within a marketing stack, especially when training AI models
• Identity Resolution: Proven experience with Identity Stitching or Customer 360 frameworks to unify anonymous digital signals with known customer records
• AI Production Scaling: Experience moving AI models or agentic workflows from experimental pilots into standardized, production-level deployments