Key Responsibilities
Provide architectural leadership for the Data & Analytics platform, including target-state direction, principles, standards, and guardrails
Guide the evolution of the AWS-based analytics environment, with particular focus on Redshift architecture, scalability, reliability, maintainability, and performance
Define and maintain best practices across ingestion, orchestration, data modeling, BI consumption, platform usage, and AI data readiness
Review, challenge, and validate solution designs proposed by development teams to ensure alignment with platform standards and enterprise architectural principles
Support governance and quality improvement through practical architecture review, design oversight, and standards adoption
Help shape platform capabilities that improve data readiness for AI/ML and LLM-related use cases, including metadata quality, discoverability, governed reuse, lineage visibility, and fit-for-purpose data preparation
Provide guidance on modern data architecture patterns relevant to AI enablement, including lakehouse architecture, feature stores, vector database concepts, and related design considerations where appropriate
Promote effective use of metadata, cataloging, and governance capabilities to improve data discovery, trust, interoperability, and cross-platform connectivity
Collaborate with platform, engineering, analytics, governance, and business stakeholders to align technical direction with enterprise priorities and delivery needs
Facilitate cross-team design discussions, technical decision-making, and trade-off analysis across multiple teams and stakeholders
Measures of Success
Improved architectural consistency across teams and platform domains
Higher quality, more scalable, and more maintainable solution designs
Stronger adoption of platform standards, guardrails, and best practices
Improved coordination between platform, development, and data modeling teams
Stronger governance and better technical decision quality across the platform
Improved reliability, maintainability, and long-term sustainability of the environment
Better metadata quality, discoverability, lineage visibility, and governance to support analytics and AI-ready data usage
Improved readiness of data assets and platform capabilities for AI/ML and LLM-related use cases
Effective support of strategic initiatives that require cross-team architectural leadership and coordination
Required Qualifications
6–8 years of experience in a similar AI data architecture, platform architecture, data architecture, solution architecture, platform engineering, or senior data engineering role within a cloud-based Data & Analytics environment
Strong experience with AWS services and architectural principles relevant to enterprise data and analytics platforms
Strong understanding of Redshift-based analytical data environments
Experience across key Data & Analytics capabilities, including ingestion, orchestration, data modeling, analytics consumption, and platform operations
Demonstrated ability to define standards, architectural patterns, and design guardrails across multiple teams
Proven experience reviewing, challenging, and validating technical solutions proposed by engineering and data teams
Practical working knowledge of SQL and analytics tools sufficient to assess technical designs and engage credibly with delivery teams
Familiarity with modern data architecture patterns that support AI/ML use cases, including lakehouse architecture, feature stores, and vector database concepts
Practical understanding of data preparation, metadata, governance, and discoverability needs that support downstream AI, ML, and LLM use cases
Preferred Qualifications
Experience with HVR and/or Fivetran in enterprise ingestion environments
Familiarity with Tableau and Power BI in governed analytics ecosystems
Experience with data fabric, data mesh, or other distributed data architecture models, including decentralized ownership and federated governance
Experience with modern metadata, catalog, lineage, or governance platforms that improve discovery and interoperability
Experience improving metadata management, cataloging, governance processes, or platform transparency
Experience with enterprise architecture practices, platform modernization, or operating model improvement
Exposure to AI/ML platform enablement patterns, including governed data provisioning for LLM use cases
Certifications in AWS, architecture, data engineering, or project/program management
Experience participating in architecture review boards, design authorities, or technical governance forums
Additional Information
Relocation Assistance Provided: Yes
