About this role
Data Architect
We are seeking an experienced Data Architect to lead the design and definition of our Machine Learning architecture and delivery approach for commercial analytics use cases, with an initial focus on Customer Lifetime Value (CLV). The architect will assess the current ML processes and requirements, define the target architecture, establish best practices, and collaborate with business and technical stakeholders to create a practical implementation roadmap. This role is focused on ML solution architecture and delivery, leveraging platforms such as Databricks and MLflow. Experience with GenAI, LLMs, or advanced AI technologies is beneficial but not mandatory.
YOUR ROLE
Assess and document the current ML requirements, processes, and technical landscape. Defihe end-to-end target architecture for ML solutions, ensuring scalability, maintainability, and business alignment. Design architecture patterns and best practices for model development, deployment, monitoring, and governance. Lead the architecture definition for the Customer Lifetime Value (CLV) use case and future ML initiatives. Collaborate with Data Scientists, Data Engineers, Product Owners, and business stakeholders to translate business requirements into technical solutions. Define data, model, and platform integration principles within the Databricks ecosystem. Establish ML lifecycle management processes using MLflow and related tooling. Support the creation of an implementation roadmap, including phases, dependencies, risks, and technical recommendations. Provide architectural guidance during implementation and ensure alignment with enterprise architecture standards. Review and validate solution designs, technical specifications, and implementation approaches.
YOUR PROFILE
7+ years of experience in Data Architecture, Data Engineering, or Solution Architecture roles. Proven experience delivering commercial Machine Learning use cases in production environments. Strong experience designing and implementing solutions using: Databricks, MLflow and Cloud-based data platforms (Azure, AWS, or GCP) Solid understanding of: Machine Learning lifecycle management, MLOps concepts and best practices, Data architecture and integration patterns and Data modeling and data governance principles Experience working with cross-functional teams including business stakeholders, data scientists, and engineering teams. Strong communication and stakeholder management skills
WHAT YOU'LL LOVE ABOUT WORKING HERE:
Flexible and dynamic work environment involving teams spread across d