About your team and position: Sundays is a high-growth, category-defining brand that has built a sizable business — without a formal analytics engineering foundation. Now, we’re investing in doing data right. We’re hiring our first Lead Analytics Engineer to build our transformation layer from scratch, establish trusted definitions across the company, and create a scalable dbt-powered foundation that turns rich customer data into durable business advantage. Reporting to our newly appointed VP of Data, this is a rare opportunity to architect the analytics stack at a company that already knows its customers deeply — and is ready to get dramatically smarter. This role is fully remote within the U.S., with travel to our Cleveland headquarters for onboarding and quarterly in-person collaboration to stay connected as a team. Your day-to-day: - Develop and maintain high-quality dbt models, tests, documentation, and workflows to create reliable transformation pipelines. - Build and optimize dimensional and semantic data models that power business-critical reporting and analysis. - Partner with data consumers and stakeholders to translate business questions into well-defined datasets and semantic definitions. - Design, build, and maintain the semantic layer for visualization tools such as Omni Analytics, Looker, or Sigma. - Ensure data quality and trust by implementing automated testing, monitoring, and observability best practices. - Collaborate with Engineering to scale pipelines, improve performance, and reduce latency in the analytics stack. - Own the data modeling strategy and governance standards, including conventions, documentation, and onboarding practices. - Mentor and guide other analytics engineers and data team members on best practices. We'd love to hear from you if you have: - 6+ years of experience in analytics engineering, data engineering, or related roles. - Deep expertise with dbt, including advanced modeling, macros and testing frameworks. - Strong SQL skills and experience with cloud data warehouses (e.g., Snowflake, BigQuery, Databricks). - Significant hands-on experience developing in modern visualization layer platforms such as Omni Analytics, Looker, or Sigma. - A solid understanding of DTC and ecommerce metrics such as CAC, LTV, cohort analysis, retention and forecasting. - Experience translating business needs into structured, reusable analytics models. - Excellent communication skills and ability to present technical concepts to business stakeholders. Nice to Have: - Experience with data observability and monitoring tools. - Familiarity with reverse ETL tools and workflows. - Python experience for orchestration or transformation tasks. **RECRUITING SCAM ALERT** We’ve been made aware of scammers impersonating Sundays for Dogs and inviting candidates to interview via Microsoft Teams. To protect yourself, please note: - We do not conduct interviews over Microsoft Teams. - All interview scheduling communications will come from one of the following domains: @ats.rippling.com (our official applicant tracking system) or @sundaysfordogs.com. If you receive outreach from any other email domain or are asked to communicate outside of these channels, it is not from our team. We strongly suggest you do not share any personal or confidential information if you are unsure about the legitimacy of the communication. If you have questions about whether a message is legitimate, contact us directly at people@sundaysfordogs.com.

About Checkmate Checkmate builds the operating system for digital ordering in restaurants, powering integrations between POS systems, delivery platforms, and restaurant brands. Our products sit at the center of how millions of orders move across systems every day making experimentation, automation, and ML-driven optimization central to our competitive advantage. We operate at the intersection of product, data, and restaurant operations where analytical rigor directly drives revenue growth, product innovation, and customer experience. Role Overview We are seeking a Senior Analytics Engineer who combines strong data engineering fundamentals with advanced analytical thinking and business acumen. This role is not just about building pipelines it’s about designing the analytical foundation that powers product decisions, experimentation, and machine learning initiatives. You will translate complex restaurant operational and customer behavior data into scalable, reliable, and insight-ready data models that drive measurable business impact. You will act as a bridge between engineering, analytics, product, and data science ensuring our data platform supports both robust technical systems and high-quality decision-making. What You’ll Own Business Analytics & Decision Enablement - Translate ambiguous business questions into structured analytical frameworks - Develop KPI definitions, metric layers, and standardized reporting logic across teams - Partner with Product and Growth teams to define success metrics and measurement strategies - Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency - Present insights and trade-offs clearly to senior stakeholders and leadership Experimentation & Advanced Analytics - Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines - Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference - Build experimentation dashboards and automated reporting systems - Partner with Data Science to productionize predictive models and design reusable ML-ready datasets - Support feature engineering and monitoring for production ML systems Analytics Engineering & Strategic Technical Leadership - Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases - Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics - Create trusted, well-documented datasets for product, finance, operations, and growth teams - Improve data quality, observability, lineage, and governance across the analytics ecosystem - Optimize performance across warehouse and transformation layers. - Drive best practices in analytics engineering across the organization - Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind - Mentor analytics engineers and elevate modeling and measurement standards - Champion a data-driven culture grounded in experimentation and measurable impact

• Translate ambiguous business questions into structured analytical frameworks • Develop KPI definitions, metric layers, and standardized reporting logic across teams • Partner with Product and Growth teams to define success metrics and measurement strategies • Deliver deep-dive analyses on ordering behavior, churn risk, marketplace performance, and operational efficiency • Present insights and trade-offs clearly to senior stakeholders and leadership • Own and scale A/B testing infrastructure, including experiment tracking and evaluation pipelines • Define best practices for hypothesis testing, power analysis, sequential testing, and causal inference • Build experimentation dashboards and automated reporting systems • Partner with Data Science to productionize predictive models and design reusable ML-ready datasets • Support feature engineering and monitoring for production ML systems • Design and maintain scalable ELT/ETL workflows that power analytics, experimentation, and ML use cases • Build and optimize dimensional data models (star schemas, data vault, etc.) to enable self-service analytics • Create trusted, well-documented datasets for product, finance, operations, and growth teams • Improve data quality, observability, lineage, and governance across the analytics ecosystem • Optimize performance across warehouse and transformation layers • Drive best practices in analytics engineering across the organization • Influence tooling decisions (Airflow, dbt, Snowflake, Spark, etc.) with scalability and business usability in mind • Mentor analytics engineers and elevate modeling and measurement standards • Champion a data-driven culture grounded in experimentation and measurable impact

• Build and maintain feature marts for machine learning pipelines, working closely with our Data Scientist to ensure production-ready data models • Design and implement data transformations using DBT, creating reliable, well-documented data pipelines from multiple source systems • Create and manage datasets that enable self-service analytics across Product, GTM, and Customer Success teams • Develop dashboards and reports in QuickSight for key stakeholders, translating business questions into actionable insights • Perform ad-hoc analyses to support product and business decisions, moving quickly to answer critical questions • Ensure data quality and reliability through testing, documentation, and monitoring • Collaborate across teams to understand data needs and deliver solutions that scale