Senior Analytics Engineer

Okta•Chicago, IL

21h•$118,000 - $182,000•Hybrid

About The Position

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.

Requirements

5–8+ years of experience in Analytics Engineering, Data Engineering, or similar roles
Strong SQL skills and experience building analytics-ready data models
Mentorship & Engineering Excellence: Mentorship, raising the technical bar, establishing organization-wide standards for dbt/SQL quality and CI/CD
Hands-on experience with dbt and Snowflake or other ETL, Modeling and database platforms
Solid understanding of data modeling principles, including dimensional modeling and semantic design
Ability to navigate highly ambiguous business challenges, translating vague, complex, or competing goals from executive stakeholders into clear, actionable, and robust data solutions
Experience translating business requirements into clear, maintainable data logic
Familiarity with SaaS metrics and Finance and People data (e.g., ARR, revenue recognition, billing, attrition etc.)
Experience with data quality, testing, and documentation best practices
Strong communication skills and ability to work across technical and business teams

Nice To Haves

Exposure to Python, R, or data processing frameworks (e.g., PySpark)
Experience with BI tools such as Tableau or Looker

Responsibilities

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