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

• Architectural Overhaul: Lead the design and execution of a massive migration from 1,200+ legacy dbt models into a modular, three-project Medallion structure (Bronze, Silver, Gold). • Systems Governance: Define the "Engine Standard" for data contracts, naming conventions, and testing frameworks. You aren't just following a process; you are building the process others will follow. • Performance & Scalability: Partner with the Principal Engineer to optimize our Snowflake footprint. You will identify systemic inefficiencies in our DAGs and warehouse usage to drive down costs while increasing query speed. • Technical Mentorship: Act as a force multiplier for the team. You will lead high-level design reviews, provide "Staff-level" feedback on PRs, and elevate the technical bar for our Senior and Mid-level engineers. • Strategic Partnership: Bridge the gap between Engineering and Business. You’ll ensure our infrastructure supports long-term growth (3–5 years out) without compromising the day-to-day execution speed of our analysts.