Digital Logic Design Jobs in Wisconsin (NOW HIRING)

From the company’s founding until now, our mission has remained the same: to make access to data as simple and reliable as electricity. With the company, customer data arrives in their warehouses, canonical and ready to query, with no engineering or maintenance required. We’re proud that more organizations continue to leverage our technology every day to become truly data-driven.

the company and dbt Labs are bringing together two industry-leading companies with a shared mission: helping organizations unlock the full value of their data. Together, we’re delivering the data infrastructure layer that helps organizations move, transform, and trust their data — from the moment data moves, through every transformation, to the context teams and AI systems rely on. the company helps organizations automate data movement across the systems, clouds, engines, and tools they rely on. dbt Labs pioneered analytics engineering, helping teams transform data into reliable, governed insights. Together, we support thousands of organizations as they build a trusted foundation for analytics, AI, and better business decisions.

As we bring our teams and technology together, we’re building on the strengths of both companies while continuing to deliver the products and experiences our customers know and trust. It’s an exciting time to join us: we’re creating a company with the scale, talent, and technology to help more organizations put their data to work with greater speed, confidence, and impact. During this transition period, you may see references to both the company and dbt Labs throughout our recruiting process as we integrate our teams, systems, and career sites.

This is a full-time, flexible hybrid position based out of our Oakland, CA office. Our hybrid work model offers a blend of remote flexibility and in-person collaboration. There is no mandate on the number of days required but we encourage employees to come in every week to collaborate.

We’re hiring a Senior Data Analyst, Revenue to support our CFO and Finance organization on some of the most complex and consequential analytical problems at the company. This role sits at the intersection of finance, data, and applied mathematics, with a focus on understanding, forecasting, and modeling the economic engine of the business.

You’ll develop sophisticated models for revenue attribution, forecasting, customer behavior, and financial performance; build the underlying data infrastructure that powers company-wide reporting; and partner directly with Finance and executive leadership on decisions that shape the business.

This is a highly quantitative role for someone with exceptional numerical intuition, rigorous logical thinking, and an unusually high bar for accuracy. You’ll work on problems where small errors can have meaningful downstream consequences, and where the best solution often requires going beyond standard reporting into mathematical modeling, statistical methods, or machine learning.

If you enjoy decomposing ambiguous business problems into precise mathematical frameworks, stress-testing assumptions, and building models that become foundational to how a company operates, this role offers the opportunity to work on some of our hardest analytical challenges.

• SQL (BigQuery / Snowflake)

• dbt / dbt State

• Looker / LookML

• Sigma / Hex / Google Sheets

• Claude Code / Codex

• Netsuite, CRM, and customer data and billing systems

• Develop sophisticated revenue models. Build, maintain, and improve complex mathematical models used to project, attribute, analyze, and understand the company’s revenue, customers, and underlying business drivers. Work in an environment with a strong emphasis on mathematical reasoning, robust logic, numerical precision, and analytical rigor.

• Own revenue attribution and financial logic. Design frameworks that accurately connect revenue to customers, products, contracts, usage, and other business dimensions, including complex attribution problems that require careful mathematical treatment.

• Build and improve revenue forecasting. Develop models that project revenue and customer behavior using historical trends, leading indicators, statistical methods, and machine learning. Continuously evaluate forecast accuracy and improve model performance over time.

• Build scalable analytical infrastructure. Create data models, pipelines, and automated systems that are reliable, optimized, and scalable, enabling timely analytics and reporting for more than 2,000 stakeholders across the company.

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