Data Business Partner - Operations

By bringing together next-gen technology and the finest live data available, Genius Sports is enabling a new era of sports for fans worldwide, delivering experiences that are more immersive, interactive and personalised than ever before. Learn more at geniussports.com.

We're upfront about where we use AI in our hiring process. Our guidelines cover our approach to the use of AI, as well as what you can expect during the interview process. Read our full guidelines here.

As a key member of our Enterprise Analytics function, you will be the technical Data Partner for Operations and play a critical role in its data-driven success. This is a hands-on analytics engineering role for a talented technician who can combine deep technical expertise with strong business judgement. You will work closely with Operations VPs to understand how the function operates, identify where better data can improve performance, and build the trusted data products needed to support decision-making, automation, and AI-enabled insights. You will develop scalable datasets in dbt on Snowflake, maintain the Operations semantic layer, and enable trusted consumption through Sigma, other BI workflows, and AI applications. This is not a reporting role. Your focus is building clean, governed, and reusable data that allows Operations teams and analysts to make better decisions with confidence.

• Act as the trusted technical data partner to Operations VPs and senior leaders.

• Translate operational processes and business priorities into scalable data models, metrics, and datasets.

• Design, build, and maintain high-quality dbt models on Snowflake.

• Own and evolve the Operations semantic layer, ensuring metrics and business definitions are consistent, trusted, and AI-ready.

• Create intuitive, well-documented datasets for Reporting Analysts and operational users in Sigma.

• Improve data quality, reliability, performance and interoperability across domains.

• Review existing operational processes and recommend changes that improve data capture, measurement and automation.

• Identify high-value use cases where data can deliver measurable efficiency, cost, service or commercial benefits.

• Promote strong standards in SQL, dimensional modelling, testing, documentation and metric governance.

• Bring clear, informed opinions on analytics engineering, semantic layers and the future of AI-enabled analytics.

• Ability to understand complex business processes and translate them into practical technical solutions.

• Confidence advising and constructively challenging senior stakeholders.

• Strong experience in analytics engineering, BI engineering or data engineering.

• Advanced SQL and data modelling capability.

• Hands-on experience with dbt and Snowflake.

• Experience designing semantic layers and governing business-critical metrics.

• Experience with Sigma or a comparable BI platform.

• A strong understanding of dimensional modelling, data warehousing, data quality and scalable data design.

• Ability to explain critical data concepts clearly to non-technical audiences.

• A strong ownership mindset, with a bias toward simplification, standardisation and measurable business impact.

• Operations leaders trust you as their technical data