Data Architect

• Design and evolve the target data architecture on Snowflake and Microsoft Azure, covering ingestion, storage, transformation, and serving layers
  
• Define data models (dimensional, Data Vault or domain-oriented) that support both BI and AI/ML use cases
  
• Architect data pipelines for batch and near-real-time processing (e.g. Azure Data Factory, Azure Functions, Event Hubs, Snowpipe, dbt)
  
• Prepare the platform for AI workloads, including feature data, training datasets, vector/embedding storage, and RAG data pipelines (e.g. Snowflake Cortex, Azure OpenAI, Azure AI Search, Azure Machine Learning)
  
• Set standards for data quality, lineage, metadata, and governance, including Entra ID–based access control, masking, and GDPR compliance (e.g. Microsoft Purview)
  
• Optimise Snowflake performance and cost through warehouse sizing, clustering, and query tuning
  
• Work directly with client stakeholders, both business and IT, to translate requirements into architecture decisions and roadmaps
  
• Guide and review the work of data engineers, and document architecture decisions (ADRs, diagrams)
  

Requirements

• 8 years in data engineering or data architecture, including at least 3 years in an architect or technical lead role
  
• Hands-on production experience with Snowflake (data modelling, security, performance tuning, cost management)
  
• Strong knowledge of Azure data services (ADLS Gen2, Azure Data Factory, Entra ID, Key Vault, networking and private endpoints)
  
• Advanced SQL and working knowledge of Python
  
• Experience designing data platforms that support ML or AI use cases
  
• Understanding of data governance, security, and privacy requirements in enterprise environments
  
• Experience working with large, international organisations and multiple stakeholders
  
• Fluent English (C1); you'll work daily with an international client
  

• dbt, Airflow/MWAA, Terraform or other IaC
  
• Snowflake Cortex, Snowpark, Azure OpenAI, or Azure Machine Learning
  
• Experience with GenAI architectures (RAG, vector databases, LLM integration)
  
• Databricks or Microsoft Fabric exposure (common alongside Snowflake in Azure estates)
  
• Snowflake SnowPro or Microsoft Azure certifications (e.g. Azure Solutions Architect Expert)
  
• Streaming (Kafka, Kinesis)
  

Benefits

• Real architectural ownership: you design the platform, not just maintain it
  
• Enterprise-scale data and AI challenges with startup flexibility