Senior Analytics Engineer

Secure Every Identity, from AI to Human Identity is the key to unlocking the potential of AI. Okta secures AI by building the trusted, neutral infrastructure that enables organizations to safely embrace this new era. This work requires a relentless drive to solve complex challenges with real-world stakes. We are looking for builders and owners who operate with speed and urgency and execute with excellence. This is an opportunity to do career-defining work. We're all in on this mission. If you are too, let's talk. 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 practices in dbt Ensure consistency across reports and dashboards through shared definitions and reusable models Apply data governance best practices, including access controls, lineage, and auditability Partner across teams to establish clear ownership and accountability for data assets Collaboration Delivery Partner with Finance, Analysts, and cross-functional stakeholders to translate business needs into data solutions Support self-service analytics by building intuitive, reusable datasets Contribute to scalable data workflows that balance immediate business needs with long-term maintainability Work within an agile environment, contributing to planning, prioritization, and continuous improvement AI and Data Mindset Demonstrate an AI-first mindset, thinking beyond data models and dashboards to how data can power intelligent systems and decision-making Understand the importance of well-modeled, well-documented, and semantically clear data for AI and LLM-based use cases A level of comfort leveraging AI-assisted workflows to improve productivity, code quality, and consistency Curiosity for emerging capabilities in platforms like Snowflake Cortex and Snowflake Intelligence, and how they can be applied to Enterprise analytics What you’ll bring to the role 5–8 ... (truncated, view full listing at source)