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 — unleashing the potential of businesses and people. The Elastic Search AI Platform, used by more than 50% of the Fortune 500, brings together the precision of search and the intelligence of AI to enable everyone to accelerate the results that matter. By taking advantage of all structured and unstructured data — securing and protecting private information more effectively — Elastic’s complete, cloud-based solutions for search, security, and observability help organizations deliver on the promise of AI.

What is The Role

Our Marketing organization is building an AI-powered intelligence system to drive strategy, insights, and revenue. We are looking for a Principal Analytics Engineer to lead the design and build of this foundation. This role is about more than just writing code—it’s about creating the semantic blueprint for how Elastic understands and interacts with its business data.

You will synthesize complex data streams into a unified, high-fidelity system that serves as the "source of truth" for the entire customer journey. By engineering a structured knowledge layer, you will enable Elastic to scale Go-To-Market (GTM) efforts in a world where data must be optimized for human reporting, predictive science, and conversational AI alike.

What You Will Be Doing

• 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.

What You Bring

• 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.

Bonus Points

• 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.

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