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Senior Analytics Engineer

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Salary

San Francisco Bay Area: $132,000 - $182,000 / CA (excluding SF Bay Area), CO, IL, NY, WA: $118,000 - $162,000

Location

Bellevue, WA; Chicago, IL; San Francisco, CA

Posted

Today

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.

What you’ll be doing

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

What you’ll bring to the role

  • 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
  • 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

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