Principal Data Architect

Role Description Tripleseat is the platform that more than 20,000 hospitality businesses use to manage group sales, banquets, catering, and event operations. We have the deepest proprietary dataset in hospitality event management: millions of events, granular F&B line items, conversion funnels, and booking patterns across every major US metro. This data is the foundation of our AI product strategy and the core of our competitive moat. We need a leader to build the data platform that turns it into intelligence. This is a foundational leadership role in a federated data operating model. You will help build and guide the organization’s first true data strategy. Analytics ownership lives with the business functions that depend on it (GTM analytics in the Sales organization, Finance analytics in Finance, and so on). You will own the end-to-end data strategy spanning platform infrastructure, analytics, and the data science capabilities that power our AI product suite. You will be the technical authority for our data platform, the architect of our canonical model, and the player-coach who raises the bar for every data practitioner at Tripleseat. You own the canonical data model, the data contracts, and the standards that hold the federation together. You are the connective tissue. You will personally architect the Snowflake warehouse, design the canonical model, write the dbt foundations, and ship the data infrastructure that powers Revenue Intelligence, our most data-intensive product. In parallel, you will partner with our existing analytics engineers and Snowflake builders to elevate their work and shape the data team as it grows. Qualifications 10+ years of hands-on data engineering and architecture experience in growth-stage SaaS. Humble, hungry, and smart with high standards and strong opinions, but low ego. Deep, current expertise in the modern data stack: Snowflake, dbt, pipeline orchestration, semantic layers, and modeling at scale. Strong opinions on data modeling (dimensional, Data Vault, wide-table, semantic layer). Direct experience with data problems that gate AI products: entity resolution, taxonomy normalization, evaluation dataset design, model monitoring, and data quality at scale. Track record of mentoring analytics engineers, data engineers, and analysts to higher levels of capability. Foundational leadership instincts to help shape a team and a function from zero. Strong product instinct for making infrastructure decisions based on product roadmap needs. Track record of partnering cross-functionally with product, engineering, finance, and go-to-market. Requirements Own the end-to-end architecture for Tripleseat’s data platform: Snowflake warehouse, dbt transformation layer, ingestion, orchestration, semantic layer, lineage, and governance. Design the canonical data model that unifies our product database with Salesforce, Zendesk, Outreach, NetSuite, and product instrumentation into a single source of truth. Make foundational decisions (storage strategy, modeling approach, performance, cost, governance) that determine whether the platform scales with our AI product roadmap. Define data contracts and quality standards that production AI features depend on. Write production dbt models, design schemas, build pipelines, and debug performance. Deliver the data infrastructure required for Revenue Intelligence: dynamic F&B minimum recommendations, menu performance insights, demand forecasting, and network benchmarking powered by data from 20,000+ venues. Solve hard data quality problems that block AI ambitions: contact deduplication, menu taxonomy standardization, and F&B reconciliation. Build a trusted reporting layer serving RevOps, Finance, and Marketing with unified customer views, pipeline analytics, ARR reporting, and demand gen attribution. Help shape the data organization as it grows, influencing hiring, leveling, and role fit across data engineering, analytics engineering, and data science. Player-coach the analytics engineers and Snowflake builders, running design reviews, code reviews, and architecture reviews. Partner closely with the SVP of Technology on org design, prioritization, and evolution of the data function. Partner with the Director of AI Products to validate data feasibility for every AI product on the roadmap. Define and execute the product instrumentation strategy by pairing with the CPO. Partner with the GTM analytics function in the CRO organization on shared models and metrics layer. Partner with the CFO organization on financial data model, ARR and pipeline reporting. Translate ambiguous business questions into a prioritized data roadmap. Benefits Competitive Medical, Dental, and Vision Insurance: Comprehensive coverage to support health and well-being. Company Paid Life Insurance, Short- and Long-Term Disability Plans: Protection and peace of mind in unforeseen circumstances. Supplemental Insurance Options: Optional critical illness, accident, and hospital plans. Commuter Benefits: Transit and parking benefits, where applicable. 401(k) with Company Match: Helping employees save and plan for retirement. Parental Leave: Support for new parents through birth, adoption, or foster care. Flexible Paid Time Off: Encouraging work-life harmony and balance. LinkedIn Learning Access: On-demand professional development courses. Pet Insurance: Supporting the well-being of furry family members. Employee Discount Programs: Exclusive savings through employee perk platforms.