Architect, AI Data Platform & Engineering

About the role:

As a Architect, you will lead the definition, governance, and continuous evolution of the enterprise data architecture vision and roadmap, ensuring strong alignment with business strategy and digital transformation goals. This role is accountable for establishing modern, secure, and scalable data platform architecture that integrate generative AI and self-service capabilities across the organization. You will assume end-to-end technical responsibility for the data platform architecture.

The role requires deep expertise in data architecture & data platforms, analytics ecosystems, and platform security. You will guide the evaluation and adoption of emerging technologies, oversee PoC initiatives, and drive the operationalization of AI driven solutions.

Working closely with data engineering, analytics, security, and business teams, you will ensure the delivery of trusted, high performing, and future ready AI driven data platforms that scale with organizational needs.

What you'll do:

Strategy, AI Engineering & Vision

Define and incorporate an AI first approach and strategy into the enterprise data architecture and vision

Establish the strategy and vision for how the entire data foundation will change with AI

Provide deep technical expertise in RAG models and semantic data search, semantic data models

Architect solutions for AI driven data engineering including unstructured data processing and AI driven dashboards & reports

Drive the operationalization of AI/ML and GenAI solutions, ensuring responsible AI practices and model governance.

Full Stack & Platform Architecture

Apply full stack knowledge (backend, identity, front-end, Auth, and APIs) as the platform moves toward application centric delivery.

Design end-to-end data architectures, including ingestion, data processing, and consumption layers.

Platform engineering by developing and overseeing PoC’s

Establish architectural principles, standards, and best practices across data modeling, integration, and metadata management

Self Service & Data Enablement

Design an architecture that is completely self-oriented to support business self serve reporting and dashboarding.

Focus on enabling tools that make processes fully self-serve to reduce dependency on central IT teams.

Architect robust RBAC (Role-Based Access Control) layers and thoughtful metadata building.

Design governed data layers and semantic models for trusted access.

Developer Productivity & Governance

Enhance developer efficiency by utilizing AI tools to streamline the creation of data foundations, ETL pipelines, and model designs, while also improving the quality of AI-powered reporting and dashboards.

Define and enforce comprehensive data governance standards, including lineage, data quality, and observability, specifically tailored for AI-driven data products.

Architect a governed semantic layer (Knowledge Fabric) to ensure centralized definition of critical business metrics and consistent outputs f