We are looking for a full-time Senior Azure Data Architect to take ownership of an enterprise Azure data platform and design the data layer supporting production GenAI, RAG, and Agentic AI applications.
You will assess and modernize existing architecture, improve data ingestion and processing, and define practical solutions for making enterprise data available for retrieval and AI consumption. This is a full-time, remote role with a 12-month engagement.
Job location: Remote / Serbia
Responsibilities
- Own the architecture of enterprise Azure data platforms, from ingestion and storage to processing, governance, and downstream consumption
- Design and improve the data and knowledge layer supporting production RAG, GenAI, and Agentic AI applications
- Define how enterprise data is extracted, prepared, enriched, stored, and made available for retrieval, including chunking, embeddings, and vector search
- Design Azure Cosmos DB solutions, covering data modeling, partitioning, indexing, consistency, Change Feed, and throughput and cost optimization
- Define and optimize Azure Synapse Analytics architecture across Pipelines, Spark, Serverless SQL, and Dedicated SQL Pools
- Design reusable, metadata- and configuration-driven ingestion frameworks for databases, APIs, and file-based sources
- Develop architectural approaches for processing structured and unstructured data, including documents, PDFs, scans, images, and free text
- Assess existing platforms and address performance, scalability, maintainability, and data-quality issues
- Define immediate improvements alongside a longer-term target architecture
- Design batch and incremental processing approaches, including incremental refresh
- Evaluate Azure SQL, Synapse, Data Factory, Databricks, and Lakehouse options based on workload, scale, cost, and business requirements
- Produce detailed architecture documentation, diagrams, and technical recommendations that engineering teams can implement
- Guide implementation and communicate architectural decisions to technical teams and business stakeholders
- Deliver actionable architectural recommendations within the first two weeks of engagement
Requirements
- Proven senior-level experience owning and delivering enterprise Azure data architecture, with recent Azure data-platform projects
- Demonstrated ownership of the data or knowledge layer behind at least one production RAG, GenAI, or Agentic AI application
- Strong hands-on Azure Cosmos DB experience, including data modeling, partition-key strategy, indexing, RU/throughput optimization, scaling, consistency models, Change Feed, and cost management
- Strong, current Azure Synapse Analytics experience across Pipelines, Spark, Serverless SQL, and Dedicated SQL Pools
- Practical experience with Azure Databricks, Azure SQL Managed Instance, SQL Server, T-SQL, Azure Data Lake/Lakehouse architecture, and Azure Functions
- Experience preparing enterprise knowledge for AI consumption, including text extraction/OCR, metadata enrichment, chunking, embeddings, and vector search or retrieval
- Proven experience processing genuinely unstructured data, including documents, PDFs, scanned content, images, or free text
- Experience designing scalable, reusable ingestion frameworks driven by metadata or configuration
- Strong data-modeling knowledge, including dimensional models, fact tables, star schemas, and analytical performance optimization
- Experience taking over and modernizing existing data platforms, balancing immediate fixes with longer-term architectural changes
- Ability to select technologies based on requirements and trade-offs, including recognizing when a service would introduce unnecessary cost or complexity
- A track record of making architecture decisions and guiding their implementation by engineering teams
- Ability to work independently with incomplete documentation and translate findings into clear, actionable recommendations
- Strong communication skills with both technical teams and business stakeholders
- Excellent knowledge of English, both written and spoken