Staff Analytics Engineer - Enterprise Data AI/ML

The Technology, Data, and Intelligence Team

Okta is the leading independent identity provider. The Technology, Data, and Intelligence (TDI) organization is the engine that powers Okta's global workforce, providing the technology and systems that enable our employees to do their best work.

The Staff AI/ML Analytics Engineer Opportunity

Okta is seeking a Staff AI/ML Analytics Engineer to join our Data & Insights team. This role is a mission-critical position responsible for defining and owning the metadata and semantic layer of our data products that power AI/ML use cases, ensuring clean, trusted, and well-modeled data that powers analytics and AI/ML across the company.

As a Staff Analytics Engineer, you will play at the intersection of data engineering, analytics, and applied AI/ML, defining business logic, join paths, and metric frameworks while also enabling AI/ML systems to understand and respond to user intent. You will partner with product, engineering, data science, and business stakeholders to deliver reliable data products that drive decision-making and improve prompt outcomes.

This role is ideal for someone who can go deep on data modeling and technical architecture, while also thinking strategically about how data and AI/ML come together to create scalable solutions with impact on business growth.

What you’ll be doing

• Own and Evolve the Semantic Layer: Design and manage the semantic layer for Okta’s Enterprise data and analytics products, including data models, labeling, and integrations.


• Define Business Logic and Metrics: Establish join paths, calculations, and metric definitions that ensure consistent, trusted reporting across the company.


• Translate User Intent into Data Structures: Build systems that map natural language queries to model-friendly structures, enabling AI/ML analytics to deliver accurate results.


• Iteratively Improve Prompt Outcomes: Analyze, tune, and refine how our AI/ML systems interpret and respond to user queries, creating feedback loops for continuous improvement.


• Metric Definition Understanding: Ensure metrics and semantic layer are understandable and explorable through development documentation and data catalog.


• Collaborate Cross-Functionally: Partner with data engineers, analytics leads, technology teams, and business stakeholders to deliver scalable, AI/ML-driven data solutions.


• Champion Best Practices: Set standards for semantic modeling, metric governance, and AI/ML analytics integration across the organization.


• Mentor and Influence: Provide thought leadership to peers and junior engineers, fostering a culture of excellence, curiosity, and data-driven innovation.

What you’ll bring to the role

• Experience: 7+ years of progressive experience in data engineering, analytics engineering, or applied AI/ML, with demonstrated leadership at the staff level.


• Technical Depth: Expertise in SQL and semantic layer technologies (e.g., LookML, dbt, Cube, AtScale, or equivalent). Strong background in dimensional modeling, ELT pipelines, and metric frameworks.


• AI Analytics Acumen: Experience with AI tools, ML, and predictive analytics to enhance decision making and automate processes.


• Business Partnership: Proven ability to partner with non-technical stakeholders to translate business requirements into robust data solutions.


• Communication Skills: Strong ability to simplify and explain complex data/AI concepts to diverse audiences.


• Education: Bachelor’s or Master’s degree in Computer Science, Data Science, Engineering, or a related field.

And extra credit if you have experience in any of the following!

• Familiarity with vector databases, embeddings, and retrieval-augmented generation (RAG).


• Experience with metadata-driven data governance frameworks.


• Background in SaaS, security, or identity industries.

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