Senior Analytics Engineer

The Global Data & Insights Team

Okta's Enterprise Data & Insights team powers the data infrastructure that drives decision-making across the company by building reliable pipelines, scalable data platforms, and production-grade data products. We partner closely with internal Data and Insights Analysts as well as external Okta Product teams to unlock business value through robust data architecture, efficient data movement, and the engineering foundations that make data trustworthy at scale.

The Senior Analytics Engineer, Enterprise Opportunity

We are seeking a Senior Analytics Engineer to support the Enterprise by building reliable, well-modeled, and trusted data for reporting, decision-making, and emerging AI use cases.
This role sits at the intersection of business context and technical execution. You will design scalable data models, define consistent business logic, and help establish a strong semantic foundation that enables both human analytics and machine-driven intelligence.
You will partner closely with Finance, People and Company Operations stakeholders, Data Analysts, and Data Engineers to ensure data is accurate, consistent, and easy to consume; whether through dashboards, self-service exploration, or AI-powered workflows.

What you’ll be doing

Data Modeling & Semantics

• Design, build, and maintain scalable data models using dbt and Snowflake
• Define and standardize core Finance, HR and Enterprise level metrics (e.g., revenue, ARR, billing, Attrition, Executive Insights, Security) with clear, governed logic
• Establish consistent modeling patterns, naming conventions, and semantic clarity across datasets
• Contribute to a shared semantic layer that supports both analytics and AI use cases

AI-Ready Data & Snowflake Ecosystem

• Prepare high-quality, well-governed datasets for use with Snowflake Cortex and Snowflake Intelligence
• Enable structured data foundations that support LLM-powered use cases, semantic querying, and intelligent applications
• Ensure data is context-rich, well-documented, and aligned with business meaning to improve AI accuracy and trust

Data Quality, Governance & Trust

• Implement robust testing, validation, and documentation