Lead, Data Architect
$160,000–$192,500 year
On-site · Charlotte, North Carolina, United States
Job Summary
Senior Enterprise Data Architect at Scout Motors to lead the design and evolution of the company’s enterprise data ecosystem, defining the data strategy, leading a newly formed data governance office, setting standards for data platforms, and enabling scalable analytics and AI capabilities. Responsible for scalable, secure, high-quality data solutions aligned with business objectives and technology roadmaps; lead MDM enabled by Stibo Systems Platform; establish and enforce data governance policies; ensure regulatory/security compliance (GDPR, HIPAA, CCPA); architect modern data platforms (cloud-first: Azure, AWS, Databricks, Stibo Systems); design batch/real-time pipelines (ETL/ELT, streaming); enable self-service analytics, BI, and AI initiatives; provide technical leadership to data engineers and architects; collaborate with stakeholders and IT leadership; travel/onsite requirements with potential in Charlotte, NC, and regular in-person engagements; salary range $160,000 - $192,500; degree requirements include Bachelor’s or Master’s in a related field; 12+ years in data architecture with 5+ years designing enterprise-scale data solutions; base salary range noted with bonus eligibility.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Information Systems, or related field
- 12+ years of experience in data architecture, engineering, or related roles
- 5+ years designing enterprise-scale data solutions
- Deep expertise in: Data modeling (dimensional, normalized, Data Vault); Data warehousing and lakehouse architectures; ETL/ELT tools (e.g., Azure Data Factory, Informatica, or similar); Master data Management; Strategic thinking and roadmap development; Strong communication and stakeholder management; Systems thinking and problem-solving; Ability to balance governance with agility; Leadership and mentoring capability; Deliverables/Success Metrics: Enterprise data architecture roadmap adoption, Data quality and accessibility improvements, Successful implementation of modern data platform(s), Reduced data redundancy and improved consistency, Business value from analytics and data initiatives
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