AI Cloud Data Architect

Photon; Île-de-France; On-site; EUR 110,000 - 160,000; Full time

Job summary

Photon is seeking an experienced AI Cloud Data Architect to design, implement and govern enterprise-scale data and AI platforms on cloud environments. The role focuses on cloud-native data architectures, AI/ML platforms, data governance and modern analytics ecosystems.

You will design data lakes, lakehouses and warehouses, establish governance and security, work with data scientists and engineers, and drive MLOps, DataOps and AI governance across the portfolio.

Qualifications

• Extensive experience in cloud data architectures and AI platforms.
• Strong Python and SQL programming skills.
• Proven ability to design scalable data ecosystems.
• Hands-on experience with Databricks, Snowflake and BigQuery.
• Knowledge of data governance, security and regulatory compliance.
• Excellent stakeholder management and leadership capabilities.

Responsibilities

• Design and implement enterprise data architectures on cloud platforms.
• Define end-to-end data strategy including ingestion, storage and governance.
• Architect AI/ML and Generative AI platforms on cloud services.
• Design scalable data lakes, lakehouses and data warehouses.
• Establish data governance, metadata, security and compliance frameworks.
• Collaborate with stakeholders, data scientists and engineers to deliver AI solutions.
• Define data standards and architecture principles.
• Evaluate new AI/cloud technologies and best practices.
• Lead architecture reviews and ensure enterprise alignment.
• Support MLOps, DataOps and AI governance initiatives.

Tools

Databricks

Snowflake

Azure

AWS

Google Cloud

BigQuery

Data Lakes

Lakehouses

Data Warehouses

AWS Glue

Kubernetes

Docker

Python

SQL

MLflow

Kubeflow

Azure ML

LLMs

Vector DBs

Data Governance

Job description

We are looking for an experienced AI Cloud Data Architect to design, implement, and govern enterprise-scale data and AI platforms on cloud environments. The ideal candidate will have strong expertise in cloud-native data architectures, AI/ML platforms, data governance, and modern analytics ecosystems. This role will drive the design of scalable, secure, and resilient solutions that enable advanced analytics, Generative AI, and machine learning use cases.

Key Responsibilities

• Design and implement enterprise data architectures on Azure, AWS, or GCP.
• Define end-to-end data strategy, including ingestion, storage, transformation, governance, and consumption.
• Architect AI/ML and Generative AI platforms using cloud-native services.
• Design scalable data lakes, lakehouses, data warehouses, and real-time data processing solutions.
• Establish data governance, metadata management, security, privacy, and compliance frameworks.
• Collaborate with business stakeholders, data scientists, engineers, and product teams to deliver AI-driven solutions.
• Define data standards, architecture principles, and best practices.
• Evaluate and recommend emerging technologies related to AI, cloud, and analytics.
• Lead architecture reviews and ensure alignment with enterprise architecture standards.
• Support implementation of MLOps, DataOps, and AI governance frameworks.

Required Skills

• Strong experience in Cloud Architecture, Data Engineering, and AI Platforms.
• Expertise in Azure, AWS, or Google Cloud Platform.
• Data Lakes
• Lakehouses
• Data Warehouses
• Hands-on experience with:
• Databricks
• Snowflake
• AWS Glue
• BigQuery
• Strong knowledge of AI/ML ecosystems and Generative AI technologies.
• Experience with Large Language Models (LLMs), RAG architectures, Vector Databases, and AI Agents.
• Proficiency in Python, SQL, and distributed data processing frameworks.
• Knowledge of data security, governance, and regulatory compliance.
• Excellent stakeholder management and leadership skills.

Preferred Skills

• Experience implementing enterprise GenAI solutions.
• Knowledge of Azure OpenAI, Amazon Bedrock, Vertex AI, or similar services.
• Experience with Kubernetes, Docker, and microservices architecture.
• Familiarity with MLOps tools such as MLflow, Kubeflow, or Azure ML.
• Experience in Banking, Financial Services, or highly regulated industries.
• TOGAF, Azure Architect, AWS Architect, or Google Cloud certifications.