Manager Data Architecture

Job Summary

The Manager, Data Architecture leads the design, governance, and evolution of the enterprise data architecture strategy within a modern cloud-native ecosystem. This role is responsible for managing architectural standards, guiding data engineering teams, overseeing platform design (e.g. Snowflake, dbt), ensuring data systems are scalable, secure, AI-ready, and aligned with enterprise governance. The position drives the transformation from traditional warehouse-centric design toward domain-oriented data products, automation, and advanced analytics enablement.

• Define and govern enterprise-wide data architecture standards, patterns, and best practices across cloud platforms.
• Lead architectural decisions across Snowflake, ELT/dbt frameworks, CI/CD pipelines, observability tooling, and security models.
• Ensure architectural consistency across ingestion, transformation, semantic, and consumption layers.
• Establish scalable domain-driven data product patterns that promote ownership, reuse, and SLA-based delivery.
• Drive logical and physical data modeling standards (dimensional, data vault, semantic layer design).
• Partner with business and analytics teams to ensure architectures support self-service analytics and enterprise reporting needs.
• Ensure data platforms are architected to support AI/ML workloads, including structured and unstructured data integration.
• Enable LLM‑ready architectures, metadata enrichment, vectorization strategies, and real‑time/event‑driven use cases.
• Support Data Science and advanced analytics initiatives through optimized pipeline and storage design.
• Embed data quality frameworks, automated testing, observability, lineage, and monitoring into architecture standards.
• Ensure compliance with enterprise governance, security, privacy, and regulatory requirements (e.g. SOX, PCI where applicable).
• Partner with Data Ops and Dev Ops to operationalize governance and CI/CD controls.
• Lead, mentor, and develop data architects and senior data engineers.
• Establish architectural review processes and technical design governance forums.
• Promote a culture of engineering excellence, automation, and continuous improvement.
• Partner with external vendors and consulting partners to align implementations with enterprise architecture standards.
• Collaborate cross‑functionally with Engineering, Martech, Data Science, Dev Ops, Security, and Product teams.
• Serve as the primary escalation point for complex architectural decisions.
• Contribute to long‑term roadmap planning for platform scalability, modernization, and cost optimization.
• Evaluate emerging technologies and assess adoption value within the enterprise ecosystem.
• Drive modernization of legacy platforms where strategically appropriate.
• Perform other job‑related functions as assigned.

• Bachelor’s Degree in Business Management, Computer Science, Industrial Engineering, or related field; or equivalent combination of experience and education.
• 8–12+ years of progressive experience in data architecture, data engineering, or enterprise data platform leadership roles.
• Proven experience designing and governing cloud‑native data platforms (e.g., Snowflake, dbt, modern ELT architectures).
• Experience leading technical teams and managing architectural standards in Agile product environments.
• Strong expertise in advanced data modeling techniques and enterprise data product design.
• Experience implementing CI/CD, Dev Ops/Data Ops practices, and observability within data ecosystems.
• Experience enabling AI/ML and advanced analytics workloads, including real‑time or event‑driven architectures.
• Demonstrated experience in modernizing legacy data systems and guiding large‑scale architectural transitions.

• Deep understanding of modern data architecture principles, cloud ecosystems, and distributed data platforms.
• Strong leadership and coaching capabilities with ability to influence across technical and business teams.
• Advanced proficiency in SQL and working knowledge of Python or similar languages for automation and extensibility.
• Strong understanding of data governance, metadata management, lineage, and quality frameworks.
• Experience…