Cloud Data Engineer

About the Role

Location: Northwest Arkansas (on-site) Employment type: Full-time, exempt We are a multi-vertical, family-owned enterprise operating since 1987, with holdings across automotive retail, heavy equipment, beverage distribution, and aviation. As we continue to acquire and integrate new businesses, we are investing in a modern, governed data platform on Google Cloud to unlock business intelligence, operational analytics, and AI-driven decision-making across every vertical. The Cloud Engineer will be a foundational hire to build our next generation data platform powering reporting, analytics, and AI use cases across our enterprise. This role will work to implement a medallion-architecture data warehouse using tools like Google BigQuery and Dataplex, ingesting data from operational systems including CDK Global DMS, ERP platforms, IoT and telematics sources, and SaaS applications across our verticals. This is a hands-on engineering role for someone who wants to shape a greenfield platform, not maintain a legacy one. What you will do Build the data platform Design and implement a medallion-architecture (bronze / silver / gold) data warehouse in BigQuery, including ingestion, transformation, and curated semantic layers. Stand up and operate Dataplex for data cataloging, lineage, data quality, and unified governance across business domains. Build batch and streaming ingestion pipelines from sources such as CDK Global DMS, ERPs, telematics, IoT devices, SaaS APIs, and on-premise databases using tools such as Dataflow, Pub/Sub, Datastream, Cloud Composer (Airflow), and Cloud Run. Develop transformation pipelines using SQL, dbt, or Dataform, with strong attention to modularity, testing, and version control. Operate and harden Implement infrastructure-as-code for all cloud resources, with CI/CD pipelines for data and infrastructure deployments. Build clear separations for Development / Testing / Production data environments. Establish monitoring, alerting, cost controls, and FinOps practices for BigQuery slot usage, storage tiers, and pipeline reliability. Implement security controls including IAM, VPC Service Controls, CMEK, column- and row-level security, and integration with our identity provider. Partner on DLP, masking, and data classification strategies that support both analytics and AI use cases (including governed sandbox environments). Enable the business Partner with vertical leaders, finance, and operations to translate business questions into well-modeled, performant data products. Build curated marts and semantic models that power BI tools (Looker, Power BI, Tableau, or similar) and self-service analytics. Prepare the platform to serve downstream AI and ML use cases, including feature stores, vector search (BigQuery, Vertex AI), and Retrieval-Augmented Generation patterns. Document architectures, data contracts, and runbooks What you bring Required experience 5+ years of professional data engineering or cloud engineering experience, with at least 2+ years on Google Cloud. Demonstrated production experience with BigQuery Strong SQL skills Solid understanding of data warehousing concepts including medallion / lakehouse architectures, dimensional modeling, slowly changing dimensions, and data contracts. Working knowledge of data security and governance concepts: IAM, encryption, PII handling, data classification, and audit logging. Preferred experience Google Cloud Professional Data Engineer certification. Hands-on experience with Dataplex (or comparable governance and catalog platforms such as Collibra, Alation, or Informatica EDC) for cataloging, lineage, and data quality. Experience implementing infrastructure-as-code (Terraform) and CI/CD for data platforms. Experience integrating data from CDK Global DMS, Reynolds & Reynolds, or similar automotive dealership management systems. Experience working in multi-entity, multi-vertical, or post-acquisition data integration environments. Familiarity with Vertex AI, Gemini, or other GenAI tooling, and patterns for governed AI use cases (synthetic data, DLP-protected sandboxes, RAG). Experience with Looker (LookML) or other modern BI semantic layers. Exposure to SIEM and log analytics platforms (Google SecOps / Chronicle, Splunk, Microsoft Sentinel) feeding into or out of the warehouse. How you work You write clean, tested, version-controlled code and treat data pipelines as production software. You think in terms of business outcomes, not just tickets, and can speak with both engineers and operators. You are direct, relationship-oriented, and comfortable in an environment where decisions move quickly and context spans multiple businesses. You are excited to build foundations rather than maintain finished systems. You lean into evolving AI tools Why this role Greenfield data platform: you will help define the architecture, not inherit someone else's. Real, varied data: automotive retail, heavy equipment, distribution, and aviation in one environment. Direct line of sight from your work to executive decisions and AI-enabled business outcomes. Stable, profitable, family-owned enterprise actively investing in technology and acquisitions. Compensation and benefits Competitive base salary commensurate with experience, performance bonus, medical / dental / vision, 401(k) with company match, paid time off, and professional development support including Google Cloud certifications.