Azure AI Data Engineer - Lakehouse & AI Pipelines

Square One Resources Limited; London; On-site; NOK 893,000 - 1,403,000; Full time

Job summary

Square One Resources Limited in London is seeking an experienced Azure AI Data Engineer to lead the design and delivery of Azure-native data platforms and AI-ready pipelines.

You will build scalable pipelines with ADF, Synapse, and Fabric, manage data across lakehouse architectures, and transform data into actionable insights for AI use cases such as RAG and vector search, collaborating with business stakeholders throughout the project lifecycle.

Qualifications

• Strong experience as an Azure Data Engineer.
• Hands-on with ADF, Synapse, Fabric and ADLS Gen2.
• Experience with Cosmos DB and NoSQL data modelling.
• Exposure to AI/GenAI data pipelines (RAG, embeddings, vector search).
• Strong Python and SQL development skills.
• Experience with data profiling and data quality frameworks.
• Knowledge of CI/CD and Azure DevOps practices.

Responsibilities

• Design and build scalable Azure data pipelines using ADF, Synapse and Fabric.
• Develop Lakehouse architectures and AI-ready data platforms.
• Engineer solutions for structured, semi-structured and unstructured data.
• Work with Azure Data Lake, Cosmos DB and Azure SQL/Synapse.
• Support AI use cases including RAG, vector search and Azure AI Search.
• Collaborate with stakeholders to translate requirements into data solutions.
• Deliver production-ready, high-performance data engineering solutions.

Skills

Python

SQL

Data modeling

Data quality

CI/CD

DevOps practices

Stakeholder collaboration

Tools

ADF

Synapse

Microsoft Fabric

ADLS Gen2

Cosmos DB

AI pipelines

Job description

Square One Resources Limited in London is seeking an experienced Azure AI Data Engineer to lead the design and delivery of Azure-native data platforms and AI-ready pipelines.

You will build scalable pipelines with ADF, Synapse, and Fabric, manage data across lakehouse architectures, and transform data into actionable insights for AI use cases such as RAG and vector search, collaborating with business stakeholders throughout the project lifecycle.