AI Data Architect
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Job Summary
The AI Data Architect will play a key role in shaping and governing the evolution of the enterprise Data & Analytics platform with a particular focus on preparing the platform and its data assets to support advanced analytics AI/ML and LLM-driven use cases. Working across platform engineering data engineering data modeling analytics governance and business stakeholders this role will define architectural standards and guardrails review and validate solution designs and help teams adopt scalable and sustainable patterns.
The platform operates in an AWS environment with Redshift as the core analytical data platform. Data is ingested through HVR (Fivetran) and internally developed batch ingestion applications orchestrated through an in-house tool and consumed through Tableau and Power BI. Within this context the AI Data Architect will provide direction across ingestion orchestration modeling BI consumption governance operational maturity and AI-oriented data enablement.
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
68 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
Relocation Assistance Provided: Yes
Required Experience:
Staff IC
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