Senior Analytics Engineer

Genius Sports•New York, NY

1d•Hybrid

About The Position

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 The Role - Senior Analytics Engineer We are hiring a Senior Analytics Engineer on our Enterprise Technology team to own and scale the semantic and presentation layers of our data platform. This is a hands-on individual contributor role focused on building high-quality, scalable data models and defining standardized metrics across the business. You will sit at the intersection of data analytics and business stakeholders, ensuring our data is structured for both performance and usability. You will directly influence how data is consumed across Product, Commercial, Finance, and Operations, enabling consistent, self-service analytics at scale. This role is expected to elevate data from a reporting function to a core business enabler and revenue driver. You will design data models, metrics, and analytics capabilities that directly influence commercial outcomes, product performance, and operational efficiency.

Requirements

6+ years in analytics engineering, BI engineering, or data engineering
Experience working in a modern cloud data stack (Snowflake preferred)
Demonstrable experience of, and technical proficiency in, some or most of DBT Cloud, Databricks, Snowflake, Gitlab, Fivetran.
Strong experience with BI tools (Sigma etc.)
Deep understanding of: Dimensional modeling
Semantic layer design
Data warehousing best practices (Snowflake)
Demonstrable experience extracting data from APIs to surface to raw layers in data warehouses
Ability to translate business questions into clean, reusable data models
Experience defining and governing business-critical metrics
Focus on performance, usability, and long-term scalability
Strong stakeholder management across technical and business teams
Proven ability to drive adoption of self-service analytics
Ownership mindset with a bias toward simplification and standardization

Nice To Haves

What Success Looks Like: A trusted, centralized semantic layer used across the company
Clear, consistent KPI definitions with no duplication or conflict
Increased self-service adoption across business teams
Reduced time to insight for Product, Commercial, and Operations
Scalable foundation for AI-driven analytics and reporting
Data is consistently used to drive revenue growth, margin improvement, and product performance decisions
Business stakeholders rely on the data platform to make decisions, not just monitor results
Analytics outputs are directly tied to commercial outcomes and measurable business impact

Responsibilities

Own the Semantic & Presentation Layer: Design and maintain core data models and metrics used across the business
Establish a single, standardized layer of business logic for reporting and analytics
Ensure consistency in KPI definitions across teams
Build Scalable Data Models: Develop and optimize models in dbt (or equivalent) on top of Snowflake
Structure data for performance, reusability, and clarity
Partner across the business to improve pipeline efficiency and reliability
Drive Self-Service Analytics: Create intuitive, well-documented datasets that reduce reliance on analysts
Improve adoption of BI tools (Sigma, or similar)
Enable business users to explore data confidently without duplication of logic
Define the BI & Metrics Strategy: Standardize metric definitions and governance across domains
Contribute to data modeling best practices and design standards
Support the evolution toward AI-enabled analytics and semantic layer exposure
Drive Business Impact Through Data: Partner with Product, Commercial, and Finance teams to define metrics that directly influence revenue, margin, and customer outcomes
Translate business strategy into measurable data models and KPIs that inform pricing, performance, and growth decisions
Identify opportunities where improved data modeling and analytics can unlock new revenue streams or optimize existing ones
Ensure analytics outputs are actionable, not just descriptive, with clear linkage to business decisions
Mentorship & Influence: Drive best practices in SQL, modeling, and BI design
Partner with stakeholders to align on definitions, priorities, and data strategy