Staff Analytics Engineer - Finance

You will be working as a Staff Analytics Engineer at Okta, where you will play a crucial role in supporting Finance by building reliable, well-modeled, and trusted data for reporting, decision-making, and emerging AI use cases. Your responsibilities will include: - Driving the architectural evolution of the Finance data models, evaluating and implementing new design patterns for long-term scalability and resilience - Designing, building, and maintaining scalable data models using dbt and Snowflake - Defining and standardizing core Finance metrics with clear, governed logic - Establishing consistent modeling patterns, naming conventions, and semantic clarity across datasets - Contributing to a shared semantic layer supporting both analytics and AI use cases You will also define the strategy for data readiness and consumption by AI/LLMs, ensuring governance and semantic clarity standards meet requirements for trustworthy automated decision-making. Additionally, you will be responsible for ensuring data quality, governance, and trust by implementing robust testing, validation, and documentation practices in dbt, maintaining consistency across reports and dashboards, and applying data governance best practices. Your qualifications should include: - 8+ years of experience in Analytics Engineering, Data Engineering, or similar roles, with at least 2 years in a Senior or Lead capacity - Strong SQL skills and experience with dbt and Snowflake - Solid understanding of data modeling principles and dimensional modeling - Ability to translate executive stakeholders' goals into actionable data solutions - Experience with SaaS metrics, Finance data, and BI tools such as Tableau or Looker - Strong communication skills and the ability to collaborate across technical and business teams Success in this role will be demonstrated by trusted, well-structured data models supporting Finance reporting, consistent metric definitions, high-quality datasets enabling self-service analytics, and a strong semantic and modeling foundation ready to power AI and intelligent applications. You will also contribute to the defined multi-quarter technical roadmap for the Finance data domain, resulting in improved data platform resilience, cost-efficiency, and scalability. You will be working as a Staff Analytics Engineer at Okta, where you will play a crucial role in supporting Finance by building reliable, well-modeled, and trusted data for reporting, decision-making, and emerging AI use cases. Your responsibilities will include: - Driving the architectural evolution of the Finance data models, evaluating and implementing new design patterns for long-term scalability and resilience - Designing, building, and maintaining scalable data models using dbt and Snowflake - Defining and standardizing core Finance metrics with clear, governed logic - Establishing consistent modeling patterns, naming conventions, and semantic clarity across datasets - Contributing to a shared semantic layer supporting both analytics and AI use cases You will also define the strategy for data readiness and consumption by AI/LLMs, ensuring governance and semantic clarity standards meet requirements for trustworthy automated decision-making. Additionally, you will be responsible for ensuring data quality, governance, and trust by implementing robust testing, validation, and documentation practices in dbt, maintaining consistency across reports and dashboards, and applying data governance best practices. Your qualifications should include: - 8+ years of experience in Analytics Engineering, Data Engineering, or similar roles, with at least 2 years in a Senior or Lead capacity - Strong SQL skills and experience with dbt and Snowflake - Solid understanding of data modeling principles and dimensional modeling - Ability to translate executive stakeholders' goals into actionable data solutions - Experience with SaaS metrics, Finance data, and BI tools such as Tableau or Looker - Strong communication skills and the ability to collaborate across technical and business teams Success in this role will be demonstrated by trusted, well-structured data models supporting Finance reporting, consistent metric definitions, high-quality datasets enabling self-service analytics, and a strong semantic and modeling foundation ready to power AI and intelligent applications. You will also contribute to the defined multi-quarter technical roadmap for the Finance data domain, resulting in improved data platform resilience, cost-efficiency, and scalability.