AI Data Architect

GE Vernova · Energy, Resources & Utilities

Bengaluru
On-site
Posted today
Data & AI

About the job

Job Description Summary

We are seeking a senior AI Data Architect to lead the architectural direction, standards, and design governance of our Data & Analytics platform. This role sits at the intersection of enterprise architecture, data engineering, and AI data readiness, and is suited to a senior individual contributor who can guide platform evolution, validate solution designs, and influence technical decisions across multiple teams. The environment is AWS-based, centered on Redshift, and supports ingestion, orchestration, modeling, BI consumption, and emerging AI/ML and LLM use cases across a complex enterprise landscape.

Job Description

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

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

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