Staff Data Engineer

Who is Keystone AI ? Keystone AI provides the first and only client experience platform for appointment-based, self-care businesses. We empower our customers to give their clients more of the magical moments that matter most. Before launching in 2016, our founders spent months interviewing salon managers and working behind front desks to understand their pain points so we could design a modern, user-friendly platform that meets the unique needs of their business. Our roots may be in hair salons, but we are built for the broader self-care industry, including many types of salons, spas, medspa, barbershops, and more. Our technology not only helps our customers survive but thrive. Take a look at how we (and YOU) can make that happen . We have an insatiable curiosity and embrace experimentation. We believe that simple solutions require the most sophistication, and we design each and every detail to maximize potential, power, and impact. Do our values match? Read through our story and what we value the most . Our team values and celebrates our diverse backgrounds. Being open about who we are and what we do allows us to do the best work of our lives. We believe in equal opportunity for all, and you should too. Come do the best work of your life at Keystone AI . We're hiring a Staff Data Engineer to architect and lead the data foundation powering Keystone AI ’s data products. In this role, you'll own our data platform, visualization, advanced machine learning capabilities, and the systems that enable data-driven products and decision-making across the company. Reporting to the Director of Data Engineering, you’ll play a critical role in architecting, designing, developing, and maintaining the data ecosystems and analytics capabilities that power customer-facing products. You’ll drive complex, high-impact data initiatives that enhance product performance and customer experience through actionable insights and robust analytical frameworks. This senior technical role demands extensive expertise in data architecture, analytics engineering, and practical machine learning applications. Domains of Ownership Data Architecture - Design, build, and maintain foundational data models that serve as the single source of truth for analytics across both internal and external stakeholders. Cross-Functional Collaboration - Partner with Product, Engineering, and Analytics teams to scope and deliver new initiatives, develop analytics features, and create data-driven customer experiences. Scalable Data Products - Develop frameworks, tools, and workflows that enhance efficiency and scalability, ensuring high standards of data quality, consistency, and performance. AI Innovation & Actionable Insights - Translate business requirements into scalable data models, dashboards, and analytical tools. Proactively identify opportunities for anomaly detection, predictive analytics, recommendations, forecasting, and AI-driven innovation.Impact-Focused Solutions - Leverage modern analytics and development tools to deliver measurable business value quickly—while maintaining long-term scalability and maintainability. Key Projects & Initiatives Customer-facing Data Products: Collaborate with Product Management and Engineering to deliver robust customer-facing reporting, dashboards, and analytics features. Snowflake Data Sharing - Design and implement secure, performant Snowflake data sharing solutions that empower customers to access and leverage data safely and efficiently. Data Visualization & Performance Optimization - Oversee data visualization platforms, optimizing data models and query performance to deliver fast, reliable, and intuitive analytics experiences. Machine Learning & AI Innovation - Develop and deploy machine learning solutions for recommendations, forecasting, anomaly detection, and predictive insights that enhance product intelligence. What You'll Do Here Technical Architecture - Lead complex data initiatives by transforming ambiguous business needs into scalable architectures, pipelines, and analytical models that deliver impact. Cross-Functional Collaboration: Partner with Product Management, Product Engineering, and Analytics to understand requirements and deliver analytics platform capabilities for both external and internal stakeholders. Data Modeling & Design: Drive key semantic modeling design decisions that balance current reporting needs with future scalability and adaptability. What You'll Need to Thrive Bachelor’s degree or higher in Computer Science, Information Technology, Data Science, or a related discipline 7+ years of professional experience in Data Engineering Proven track record of delivering data products, data platform capabilities in partnership with Product teams Deep expertise with modern data stack technologies: DBT, Snowflake, SQL, Python, and BI tools like Looker/Omni. Strong understanding of modular and reusable data modeling best practices (e.g., star and snowflake schema design) Comprehensive knowledge of data