Staff Analytics Engineer
OktaSecure 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.
About Okta
At Okta, our mission is to enable any organization to use any technology, making the world a more secure and connected place. We are the leading independent provider of identity for the enterprise. We work with an incredible array of customers, from the largest enterprises to the most innovative startups, to help them securely connect their people to technology. As we enter a new phase of growth, our AI and data capabilities will be a critical pillar of our success, powering secure and scalable products that serve both our customers and employees.
The Opportunity
We are seeking a Staff Analytics Engineer to support Finance 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 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 Do
Data Modeling & Semantics
- Drive architectural evolution of the Finance data models, evaluating and implementing new design patterns to ensure long-term scalability and resilience.
- Design, build, and maintain scalable data models using dbt and Snowflake
- Define and standardize core Finance metrics (e.g., revenue, ARR, billing) 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
- Define the strategy for data readiness and consumption by AI/LLMs, ensuring that governance and semantic clarity standards meet t
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