Principal Analytics Engineer

Role Overview

As a Principal Analytics Engineer at Elastic (full remote), you will design and build the core analytics infrastructure that powers Elastic’s marketing intelligence. You’ll create the data foundation for AI-powered agents, align metrics across teams, and mentor other engineers.

Key Missions

• Architect the Foundation: Build the core BigQuery and dbt infrastructure that transforms raw marketing signals into agent-ready data products.
• Enable AI & Agents: Develop a semantic layer / structured knowledge base so AI agents can accurately query and reason over business data.
• Map the Customer Journey: Integrate disparate signals (digital, product, sales) into a unified lifecycle model from discovery to revenue.

Responsibilities

• Align with cross-functional teams on shared metrics and ensure consistent measurement.
• Mentor and coach engineers through complex architectural and data challenges.
• Turn technical work into business value, translating data/engineering debt into measurable outcomes.

Requirements / Profile

• Data as infrastructure: Experience with data contracts, automated data quality monitoring (DQM), and governance frameworks to ensure secure, consistent, reliable metrics.
• Systems & design thinking: Ability to understand complex data ecosystems and design simple, extensible architectures.
• Architectural design: Build interconnected systems by working backward from desired outcomes.
• Data-as-a-product mindset: Ensure data is discoverable and reliable for both humans and AI agents.
• Technical proficiency: Deep experience with BigQuery and dbt, plus semantic layers (e.g., dbt Semantic Layer / Vortex AI).
• Automation / LLM-assisted workflows for the data modeling lifecycle.
• GTM fluency: Understand how technical data structures map to acquisition, attribution, and revenue.
• Marketing science foundations: Familiarity with Marketing Mix Modeling (MMM), causality, or incrementality.
• Privacy & ethics: Knowledge of GDPR/CCPA and managing consent/privacy in marketing/AI contexts.
• Identity resolution: Experience with Identity Stitching or Customer 360.
• AI production scaling: Move pilots/experiments into standardized production deployments.

Nice-to-haves

• Experience integrating multi-source signals into customer lifecycle models.
• Prior work deploying semantic-layer and agentic workflows at production scale.