• Own architecting, optimizing, and transforming finance and product centric data models in DBT for flexible analysis by Data Analysts / Scientists and self-service by business stakeholders. • Design data validations to ensure data integrity throughout our pipelines, and assure the accuracy of our reporting. • Create transparency throughout the full data pipeline by establishing foundational processes with upstream data producers and downstream data consuming tools (BI, Reverse ETL, experimentation). • Advance agentic tooling and other automation efforts that help the team spend less time investigating data issues, and more time doing analyses. • Apply software engineering best practices like version control and continuous integration to the analytics code base. • Work closely with our Data Engineers, Analysts, Data Scientists, Diamond (Baseball/Softball) Sports, and Finance teams to build foundational models around new sport specific features, monthly recurring revenue, and subscription forecasting. • Support cross functional core metrics and data dependencies with leadership visibility.

• Own the Gold layer of the medallion architecture: define, version, and maintain business metrics and semantic models. • Build and maintain executive dashboards and visualizations that communicate key insights effectively. • Define and enforce best practices for data visualization, storytelling, and stakeholder adoption. • Build and maintain data marts and semantic layers that serve multiple domains across Axios. • Review and certify dashboards for accuracy, consistency, and adherence to standards. • Collaborate with data engineering to ensure Silver layer tables meet business needs. • Implement data quality tests, documentation, and lineage tracking to ensure trust in analytics outputs. • Serve as a bridge to data science, ensuring feature stores and model outputs are well-documented and reusable. • Contribute to the hub-and-spoke model: rotate through responsibilities such as intake, quality assurance, and participation in the analytics hub, while serving as a strategic partner to stakeholders, helping them shape the right questions and identify the decisions their data should inform.

Hi Auto is the most trustworthy AI order taker for drive-thrus. Built for scale. Hi Auto is a reliable AI order taker for drive-thrus, purpose-built for high-volume QSRs. It helps brands consistently deliver faster, error-free service with over 93% order completion and 96% accuracy, at scale. Growing quickly, Hi Auto has the largest number of voice AI drive-thru restaurant deployments, competing directly with both big tech and restaurant tech players.  We are seeking a Staff Engineer in Europe to join our Infrastructure team. About the role:  Our backend is developed all in Python running in the cloud over k8s. It's not a typical backend interacting with a UI frontend, handling audio streams in real-time. Our systems integrate with a large number of third party services. Because of the myriad of challenges and the fast pace of a startup, each team member gets to experience a lot of professional growth and ownership. In this role, you will: - Own parts of our core backend infrastructure, which is a complex, real-time distributed system written in Python and Rust - Own big initiatives end-to-end and deliver them with minimal guidance - Troubleshoot highly complex production issues - Constantly improve our development velocity while maintaining a very high reliability standard

• Design, build, and maintain ETL/ELT pipelines from multiple data sources (Shopify, Meta, Google, CRM, etc.) • Own and manage the data warehouse (e.g. BigQuery, Snowflake, Redshift) • Ensure reliable, scalable, and efficient data flow across systems • Monitor and improve pipeline performance, cost, and reliability • Build and maintain data models (dbt or equivalent) • Structure raw data into clean, business-ready datasets (fact/dimension tables) • Define and standardize core business metrics across teams • Enable a consistent single source of truth • Implement validation checks and monitoring to ensure data accuracy • Identify and resolve discrepancies across platforms • Maintain documentation for data definitions and logic • Improve overall trust in data across the organization • Support dashboarding and reporting (Looker, Power BI, etc.) • Partner with marketing, product, and ops teams to deliver clean datasets • Enable faster and more accurate analysis across the business