Staff Analytics Engineer - Finance

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 the requirements for trustworthy and responsible automated decision-making.
• 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

• Define and own the multi-quarter technical roadmap for the Finance data domain, aligning data architecture decisions with executive business objectives and anticipating future growth and regulatory needs.
• 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 Finance analytics

What You Bring

• 8+ years of experience in Analytics Engineering, Data Engineering, or similar roles, with at least 2 years operating in a high-impact Senior or Lead capacity.
• Proven track record of defining, driving, and delivering a multi-quarter technical roadmap for a critical data domain (e.g., Finance, Growth).
• Mentorship & Engineering Excellence: Mentorship, raising the technical bar, establishing organization-wide standards for dbt/SQL quality and CI/CD.
• Strong SQL skills and experience building analytics-ready data models
• Hands-on experience with dbt and Snowflake
• 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
• Familiarity with SaaS metrics and Finance data (e.g., ARR, revenue recognition, billing)
• Experience with data quality, testing, and documentation best practices
• Exposure to Python, R, or data processing frameworks (e.g., PySpark) is a plus
• Experience with BI tools such as Tableau or Looker
• Strong communication skills and ability to work across technical and business teams

What Success Looks Like

• Trusted, well-structured data models that reliably support Finance reporting
• Consistent metric definitions across teams and tools
• High-quality, well-documented datasets that enable self-service analytics
• A strong semantic and modeling foundation that scales with the business
• Data that is not only accurate for reporting, but ready to power AI and intelligent applications
• The Finance data domain operates on a defined multi-quarter technical roadmap, resulting in demonstrable improvements in data platform resilience, cost-efficiency, and scalability.

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