Senior Analytics Engineer, FinOps

We are seeking a Senior Analytics Engineer, Finance Data to design and build scalable, governed data models that support financial reporting and analytics. This role sits at the intersection of Analytics, Data Engineering, and Finance. You will work closely with Finance and technical teams to understand complex reporting and operational needs, investigate existing data and business logic, and translate that knowledge into reliable, reusable data products in Databricks. The immediate focus is redesigning how financial data is structured, governed, and delivered across the organization. This role will help modernize the Finance data environment by building scalable data models, reducing reliance on report-specific logic, and creating new data solutions that enable Finance to take greater advantage of Databricks, automation, and AI-enabled capabilities. This is a highly hands-on role for someone who can work through ambiguity, reason critically about data, and take solutions from discovery through production implementation. This position is open to candidates in the Seattle area. You will have a hybrid remote/in-office schedule where you will work from our casual, pet-friendly office at least 3 days a week. Key Responsibilities Design and build a scalable, governed financial data mart in Databricks that supports reliable financial reporting and analytics. Partner with Finance and subject-matter experts to translate complex financial requirements and business logic into reliable data solutions. Investigate existing data, reporting logic, and processes to identify inconsistencies, simplify transformations, and establish standardized definitions. Centralize shared financial definitions, business rules, and transformations within reusable data models to support consistent reporting across the organization. Build reconciliation, validation, and data-quality processes to ensure financial data is accurate, traceable, and audit-ready. Partner with Data Engineering and Analytics teams on upstream dependencies, architecture decisions, and adoption of governed financial data products. Troubleshoot complex data issues and continuously improve the reliability, maintainability, and scalability of Finance data solutions. Identify and develop opportunities to modernize Finance processes and analytics through Databricks, automation, and AI-enabled solutions. Required Qualifications 5 - 8 years of demonstrated experience in analytics engineering, data engineering, or data modeling, with a track record of independently designing and delivering production data solutions. Advanced SQL skills and experience working with complex relational and transactional data. Strong proficiency in Python and PySpark for data transformation, pipeline development, and analysis. Hands-on experience developing production data solutions in Databricks. Strong understanding of dimensional modeling, including fact and dimension design. Strong understanding of data…