Job DescriptionThe Senior Manager, Data and Information Architecture leads the strategy, design, and governance of enterprise data architecture to enable scalable, secure, and high-quality data foundations across the organization. This role owns the definition and evolution of data architecture standards, information models, and data platform integration patterns that underpin analytics, AI, and operational systems.
Operating within the broader Data & AI organization, this role translates business and technology priorities into cohesive, AI-ready data architecture strategies. The position ensures alignment across data engineering, analytics, AI/ML, and enterprise platforms, and plays a critical role in establishing a modern, governed, and interoperable data ecosystem that supports enterprise-scale data, AI, and digital transformation initiatives.
Responsibilities- Define and execute the enterprise data architecture strategy aligned with organizational data, analytics, and AI objectives.
- Partner with business leaders and Digital Transformation teams to establish unified, trusted data sources (Master Data / "Golden Record"), improve cross-platform integration, and ensure high data quality for digitized processes.
- Establish enterprise standards for data modeling, information architecture, integration, metadata management, and data lifecycle management.
- Own and evolve the enterprise data architecture roadmap, balancing scalability, performance, governance, and cost efficiency.
- Define canonical data models, semantic layers, and domain-oriented architectures (e.g., data mesh, data products, domain-driven design).
- Establish architecture patterns that support advanced analytics, AI/ML, knowledge management, semantic retrieval, and emerging agent-based systems.
- Design and govern enterprise data platforms including data lakes, data warehouses, lakehouses, and real-time/streaming systems.
- Ensure alignment between data architecture, AI/ML platforms (e.g., feature stores, model pipelines), and analytics ecosystems to enable reusable and scalable data products.
- Define and enforce enterprise data governance policies, standards, and best practices.
- Drive measurable improvements in data quality, consistency, and trust across critical data domains.
- Partner with security and compliance teams to ensure adherence to privacy, regulatory, and enterprise security requirements.
- Establish and operationalize data stewardship, ownership, and accountability models.
- Partner closely with Data Engineering, Analytics, AI/ML, and IT teams to ensure successful implementation of architecture standards and solutions.
- Oversee the architectural integrity, scalability, and lifecycle management of enterprise data platforms.
- Enable efficient, governed, and scalable data access for analytics, AI, and operational use cases.
- Lead architecture and design reviews to ensure adherence to standards and long-term sustainability.
- Build and lead a high-performing data architecture function focused on innovation, standardization, and business value.
- Establish reusable architecture patterns, frameworks, and playbooks for enterprise adoption.
- Attract, retain, and develop top-tier data architecture and engineering talent.
- Reports directly to the VP of AI, Machine Learning and Data Services.
- Owns and enforces enterprise data architecture standards across data engineering, analytics, and AI/ML teams.
- Leads or co-leads enterprise data platform evaluation and selection decisions with executive visibility.
- Influences design and investment decisions across the full enterprise data and AI platform portfolio.
Qualifications- Bachelor's degree in computer science, Information Systems, Data Engineering, or related field.
- 10+ years of experience in data architecture, data engineering, or enterprise data management, including 4+ years of leadership.
- Strong expertise in modern data architecture patterns (data warehouse, lakehouse, data mesh, real-time/streaming architectures) to enable scalable, AI-ready ecosystems.
- Deep experience in data modeling (conceptual, logical, physical), semantic layers, and metadata management.
- Proven experience designing and scaling enterprise data platforms in cloud environments (Azure, AWS, Snowflake, Databricks, etc.).
- Strong understanding of data integration patterns (ETL/ELT, APIs, event-driven architectures).
- Demonstrated ability to align data architecture with business strategy and deliver enterprise-scale outcomes.
- Excellent stakeholder management, communication, and leadership skills in matrixed environments.
Preferred- Experience with modern data platforms (Snowflake, Databricks, Azure Synapse).
- Familiarity with data governance tools, data catalogs, lineage, and MDM platforms.
- Experience with domain-oriented architectures (data mesh, data products).
- Exposure to AI/ML data pipelines, feature stores, and data architectures supporting GenAI use cases.
- Experience in manufacturing or complex enterprise environments with highly heterogeneous data sources.
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