Senior Data Architect

About the role

This job description is intended to describe the general nature and level of work being performed by people assigned to this classification. It is not intended to be construed as an exhaustive list of all responsibilities, duties and skills required of personnel so classified . Job Summary The Senior Data Architect is responsible for architecting Northeastern University's transition from legacy data structures to a modern, scalable, AI‑ready data architecture. This role conducts deep assessments of existing systems-including Banner, Workday, Salesforce, ServiceNow, Snowflake, and Azure-and designs future-state structures that support data products, analytics, automation, and AI/ML enablement. Establishes enterprise data standards, models, governance structures, and integration patterns, ensuring that data is trustworthy, discoverable, interoperable, secure, and prepared for advanced use cases such as RAG, vector search, semantic layers, and agentic workflows. *Applicants must be authorized to work in the United States. The University is unable to work sponsor for this role, now or in the future. Minimum Qualification Knowledge and skills required for this position are typically acquired through the completion of a Bachelor's degree in Information Systems, Computer Science, Engineering, or a related field and ten or more years of progressive experience in data architecture, enterprise data modeling, or data engineering. Proven experience modernizing legacy data environments and designing cloud-native, AI-ready data architectures. Experience working with large, complex enterprise systems (e.g., Banner, Workday, Salesforce, ServiceNow, Snowflake, Azure). Demonstrated leadership in cross-functional initiatives involving data governance, analytics, and platform modernization. Ability to translate complex technical concepts into business-aligned architectural decisions. Strong communication and data storytelling skills for senior leadership and non-technical stakeholders. Effective collaborator with demonstrated ability to drive alignment across distributed teams. Strong problem-solving, analytical thinking, and architectural documentation skills. Ability to set and enforce standards, patterns, reusable components, and governance practices. Technical Competencies: Expertise in modern data architecture patterns (data products, lakehouse, domain-driven design, semantic layers). Deep knowledge of Snowflake, Azure SQL, Databricks, or similar cloud data platforms. Proficiency with DBT, Fivetran, Informatica, or equivalent ELT/ETL tools. Experience with event-driven and API-based integrations (Kafka, EventHub, microservices). Knowledge of AI/ML foundational components: vector databases, feature stores, RAG pipelines, metadata management. Strong understanding of data modeling (conceptual, logical, physical), master data management, and data quality frameworks.