Azure Data Architect | Croatia-only | Full-remote

We are looking for an Azure Data Architect who can take ownership of the architecture and delivery of Azure data solutions, working from documentation, business requirements, and technical direction prepared by the Principal Engineer.
Demonstrated capability matters more than job titles. The essential requirement is the ability to work independently, make sound architectural decisions, and deliver production-ready solutions. Strong Azure data architecture experience and practical experience designing the data layer behind production GenAI, retrieval-augmented generation (RAG), or agentic applications are required.
What you will do
Translate documented requirements into implementation-ready data architecture and carry it through implementation, testing, and delivery. You will be responsible for planning your work, making architectural and implementation decisions within the project’s technical direction, and delivering without continuous step-by-step supervision.
Design and modernise Azure data platforms, including data ingestion, storage, processing, and retrieval. Our work includes enterprise data systems and the supporting data infrastructure for GenAI, RAG, and agentic applications.
Develop scalable ingestion and processing approaches, including metadata-driven pipelines and the handling of unstructured data.
Consult with the Principal Engineer, clients, and delivery teams to clarify requirements, explain architectural decisions, and resolve technical questions.
What we expect
Practical experience taking ownership of Azure data architecture, with the ability to translate business and technical requirements into maintainable, implementable solutions.
Evidence of your personal contribution to production systems. You should be able to explain the architecture you designed, the decisions you made, the technologies you selected, and the implementation work you owned.
The ability to investigate problems, validate your implementation, communicate progress, and raise blockers early. Working independently does not mean avoiding questions: recognising when a requirement needs clarification or a decision needs discussion is part of the responsibility.
Ownership of the quality and completeness of your work, including data quality, performance, reliability, and operational readiness.
Technical requirements
Azure data architecture: Strong practical experience designing and modernising enterprise Azure data platforms. Ability to define data flows, component responsibilities, integration points, and storage and processing approaches, and document them clearly enough to support implementation.
Azure Cosmos DB and Azure Synapse Analytics: Practical experience with both technologies is essential. You must be able to explain how you have used them, the architectural decisions involved, and the work you personally owned.
Data engineering and lakehouse platforms: Experience with Azure Databricks, data lakes, and lakehouse architectures. Ability to design scalable ingestion and processing pipelines, including metadata-driven approaches and the handling of structured and unstructured data.
SQL and relational data: Practical experience with Azure SQL Managed Instance, SQL Server, and T-SQL. Understand data modelling, indexing, transactions, query performance, and the implications of schema and platform changes.
GenAI, RAG, and agentic data infrastructure: Production experience designing the supporting data layer for these applications, including preparing unstructured data for retrieval and defining the storage and vector-search components. You must be able to identify the technologies used and explain how the data architecture supported the application’s requirements.
Integration and Azure Functions: Experience using Azure Functions within data solutions and integrating external systems and data sources. Understand data movement, processing, error handling, and the operational requirements of these integrations.
Delivery and operations: Ability to use Git, test and debug data pipelines and integrations, and take architecture through to a working deployment. Understand configuration and secrets, access control, connectivity, logging, monitoring, and troubleshooting in production.
Depth of experience may vary by area. You do not need equal expertise in every supporting technology, but you must have the required Azure platform and production AI data architecture experience, understand how the components fit together, and investigate unfamiliar components without continuous supervision.

Work arrangement
Yes