Business Intelligence Engineer • Mid-Level

About the role

As an early member of our Finance Analytics and Business Intelligence team, you will play an instrumental role in our company’s mission of building safe and beneficial artificial intelligence by establishing robust analytics engineering and business intelligence capabilities for our Finance & Accounting organization. In this unique company, technology, and moment in history, your work will be critical to ensuring accurate financial reporting, streamlining accounting processes, and supporting our financial operations as we deploy safe, frontier AI at scale to the world. 

In your role, you will use advanced analytics to drive strategic decision-making and sound operations for our Finance & Strategy and Accounting teams. You'll work at the intersection of finance, analytics, and data infrastructure to develop automated reporting systems, data quality frameworks, and scaled insights that support our various business models and entities. You’ll partner closely with Data Infrastructure, Finance Systems, Data/Analytics Engineering, and Finance & Accounting. You've worked in cultures of excellence in the past and are eager to apply that experience to building robust and scalable systems and processes as our company goes through a phase of rapid growth.

Note: we are hiring for multiple positions within the team, and you will be considered for all openings by applying to this role.

Responsibilities:

• Create processes and best practices for financial data management, governance, and reporting
• Build and maintain auditable, automated financial reporting systems, dashboards, and solutions that provide real-time insights to accounting and finance leadership
• Develop sophisticated revenue recognition analytics and monitoring to support our subscription- and consumption-based business model
• Build and maintain canonical datasets, dashboards, and scaled insights solutions that provide real-time insights to finance and executive leadership
• Partner with Finance & Strategy and Accounting teams to streamline close processes and financial reporting
• Partner with Data Infrastructure, Analytics Engineering, and Financial Systems teams to develop scalable data pipelines and infrastructure supporting key financial processes for revenue recognition, compute allocation, and consolidated reporting
• Champion data-informed decision making across the organization

You may be a good fit if you:

• Have 8+ years of experience in finance analytics, data engineering, or business intelligence with demonstrated experience partnering with Finance, Accounting, Data Infrastructure, and Finance Systems teams
• Are highly proficient in SQL and Python for data manipulation, analysis, and automation
• Have extensive experience with BI visualization tools such as Looker or Tableau; and have experience with cloud platforms (AWS, GCP) and modern data stack tools (dbt, Airflow, etc.)
• Have a proven track record of collaborating with finance stakeholders to deliver impactful analytical solutions and process improvements
• Excel at translating complex business requirements into technical solutions and can communicate effectively with both technical and non-technical stakeholders
• Are detail-oriented with a commitment to data accuracy and have experience building robust, scalable analytical solutions
• Thrive in fast-paced environments and can manage multiple priorities while maintaining high-quality deliverables
• Have experience with data modeling, ETL processes, and building automated reporting solutions

Strong candidates may have:

• Relevant certifications in financial systems or business intelligence platforms
• Prior experience in high-growth technology companies or startups
• Knowledge of statistical analysis and advanced analytics techniques
• Experience with financial planning and analysis (FP&A) processes and systems
• Experience with complex compute attribution models
• Experience with GAAP vs. non-GAAP financial reporting
• Familiarity with data governance best practices and experience implementing data quality frameworks