Senior Data Architect with AWS and Databricks, Data Platform
About the Position
We are looking for a Senior Data Architect to design and evolve a modern, scalable cloud data platform on AWS with Databricks. This role combines hands-on architecture, data platform design, and cross-functional collaboration. You will be involved in defining data architecture standards, enabling scalable analytics solutions, and supporting data driven decision making across the organization.
About the Project
The project focuses on building a centralized cloud data platform that supports large scale data ingestion, processing, and analytics workloads. The platform enables advanced reporting, analytics, and data science use cases across multiple domains.
Responsibilities
- Design and evolve end-to-end data architecture on cloud platforms
- Define data models, pipelines, and architectural standards for analytics and reporting
- Lead architectural decisions for data ingestion, transformation, and storage using Databricks
- Collaborate with data engineers and platform teams to ensure scalability and reliability
- Support data governance, security, and compliance frameworks
- Review implementations and ensure alignment with architectural principles
- Communicate complex architectural concepts to both technical and non-technical stakeholders
Requirements
- Proven experience working as a Data Architect in complex data environments
- Hands on experience designing data platforms on AWS
- Practical experience with Databricks and Spark based data processing
- Strong knowledge of data modeling, data warehousing, and analytics architectures
- Experience working with large scale data pipelines and cloud native services
- Strong programming skills (Python, SQL)
- Professional working proficiency in French
- Professional working proficiency in English
Nice to Have
- Experience with data governance or data quality frameworks
- Exposure to real time or streaming data architectures
- Experience working with data science or machine learning teams
- Background in regulated or enterprise scale environments
Technologies
AWS (S3, Redshift), Databricks, Spark / PySpark, dbt, Python, SQL, Git, MLflow, CI/CD pipelines, data orchestration tools, data governance and security tooling
