Staff Analytics Engineer

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. #Hybrid_Bengaluru #LI_Linkedin P24714_3415073 The Okta Experience Supporting Your Well-Being Driving Social Impact Developing Talent and Fostering Connection + Community We are intentional about connection. Our global community, spanning over 20 offices worldwide, is united by a drive to innovate. Your journey begins with an immersive, in-person onboarding experience designed to accelerate your impact and connect you to our mission and team from day one. Okta is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, ancestry, marital status, age, physical or mental disability, or status as a protected veteran. We also consider for employment qualified applicants with arrest and convictions re