Principal Data Architect

Tidal Wave Auto Spa is one of the fastest growing car wash chains in the country and is a recognized leader in the industry with locations nationwide. Our wave of success began in 2004 in the small town of Thomaston, GA, which is where Tidal Wave Headquarters calls home. Tidal Wave Auto Spa is a national brand that is forecasted to grow at a rapid rate for years to come, so we are aggressively pursuing individuals with exceptional talent and leadership qualities. Our goal is to redefine the car wash industry with the latest technology, top-notch friendly service, and unwavering dedication to its employees!POSITION SUMMARYTidal Wave Auto Spa is seeking a Principal Data Architect who will design, own, and evolve the centralized data architecture that powers the entire company’s intelligence, reporting, operational visibility, and future technology capabilities.This role is the keystone technical position within the data organization.You will architect and build an enterprise-grade, centralized data ecosystem used by Operations, Marketing, Finance, Executive Leadership, and our Data Partner team. Your work will enable scalable growth, data accuracy, real-time visibility, multi-entity modeling, and high-performance analytics across thousands of employees and millions of transactions.This position blends:Data ArchitectureSoftware EngineeringData WarehousingBackend EngineeringSystems IntegrationPerformance OptimizationData GovernanceYou must be able to build end-to-end systems — from raw ingestion to dimensional modeling, from centralized data architecture to real-time analytics, from schema design to repeatable validation frameworks.This role sits at the foundation of data reliability, speed, and trust across the enterprise. Every insight and every operational decision relies on the systems you build.WHAT YOU WILL OWN1. Centralized Enterprise Data Architecture (End-to-End Ownership)Architect, build, and maintain the company’s centralized data warehouse used for all analytics.Implement dimensional and semantic models based on established architectural and modeling frameworks.Build secure, multi-entity data structures to support segmentation and controlled accessOwn schema design, data lineage, metadata strategy, and governance standardsEstablish modeling, naming, and data quality practices for the entire organization2. Collaboration With Data Partners (KPI Logic & Requirements)Partner with Data Partners to define KPI logic, business rules, and metric consistencyTranslate business requirements into scalable technical architectureValidate that models support analytical needs across Operations, Finance, Marketing, and Executive leadershipEnsure the architecture anticipates and supports future reporting, analytics needs, and software solutionsYour work enables the Data Partners to transform data into insights with speed, accuracy, and confidence.3. Data Engineering LeadershipLead the Data Engineer(s), providing technical direction, standards, and mentorshipOwn ETL/ELT workflows, orchestration patterns, error-handling, and monitoringBuild scalable ingestion frameworks for high-volume, high-frequency dataDevelop high-performance transformation logic optimized for speed and reliability4. Integration & Source System EngineeringIntegrate systems via APIs, webhooks, streaming events, and secure batch processesCreate resilient, auditable ingestion frameworks with fault toleranceReconcile and validate data across all systems with automated checksHandle structured, semi-structured, and unstructured data with ease5. Software & Engineering CraftsmanshipYou must be fluent and deeply experienced in:C#/.NET, Python, and similar languagesExpert SQL (tuning, indexing, partitioning, caching, compression)API design and consumptionCloud data warehousing (Snowflake strongly preferred)DevOps, CI/CD pipelines, Git, and deployment automationReal-time and near-real-time data delivery patternsYour code must be optimized, elegant, maintainable, and fast — capable of handling complex queries in under a second.6. Reporting Foundation & Semantic ModelingBuild and maintain the enterprise semantic layer used by BI developers and Data Partners.Build models that balance performance, clarity, and business usabilitySupport both on-demand and real-time reporting scenariosEnsure data structures scale as the business grows7. Data Governance & Validation EngineeringBuild and enforce enterprise data standards and documentationDefine rules, logic, and source-of-truth structures for all major KPIsImplement automated data validation and reconciliation toolsEstablish continuous monitoring and anomaly detection systems8. High-Performance System OptimizationDeliver sub-second query performance on highly complex datasetsSelect optimal storage strategies, compute sizing, and cost-efficient warehouse designContinuously refine models, transforms, and query patterns9. Strategic System Architecture for Future GrowthDesign architectures that scale with rapid expansionB