• 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

Paired is a global recruiting agency that pairs remote work with top-tier talent. We help individuals from around the world connect with great companies that are looking for their specific skill set. Our mission is to provide great jobs to talented people, no matter where they are located. About Our Clients Our client is a growing direct-to-consumer e-commerce brand focused on delivering high-quality products and strong customer experiences. As the business scales, they are looking for a Analytics Engineer to build and own our data foundation end-to-end. This role is responsible for designing data pipelines, managing the data warehouse, and creating reliable, structured datasets that power decision-making across marketing, product, and operations. This is not a reporting-only role. The focus is on building scalable data systems that ensure accuracy, consistency, and a single source of truth across the business. Responsibilities - 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