Azure Data Engineer – Fabric & Synapse Cloud Data Pipelines

JATO Dynamics Limited

Uxbridge

On-site

GBP 65,000 - 90,000

Full time

3 days ago

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Job summary

JATO Dynamics Limited in the UK seeks an Azure Data Engineer with Microsoft Fabric experience to join our agile delivery teams. You will design, build, and optimise scalable data pipelines transforming complex datasets into valuable commercial insights.

Collaborating with Product, Architecture, Operations and Engineering, you will advance platform innovation, implement DevOps practices, and contribute to data architecture and quality across Azure-based data lakes and warehouses.

Qualifications

3+ years' experience building ETL/ELT pipelines using Azure Data Factory and Azure Synapse Analytics.
Experience with Synapse Serverless and Dedicated SQL Pools.
Strong understanding of data warehousing, data lakes, and end-to-end data pipelines.
Proficiency in SQL, stored procedures, and working with relational and NoSQL databases.
Hands-on experience with PySpark and Microsoft Fabric.
Knowledge of DevOps, CI/CD, Agile delivery, and data quality best practices.
Strong analytical, problem-solving, and troubleshooting skills.
Experience with Python, Power BI, Power Automate, Cosmos DB, or MongoDB is advantageous.

Responsibilities

Design, build, and maintain scalable ETL/ELT pipelines using Azure Data Factory, Synapse Analytics, and Microsoft Fabric.
Ingest, transform, and store data in Azure cloud platforms to create trusted data assets.
Develop and deliver data products that meet business and customer requirements.
Collaborate with Product, Architecture, Operations, and Engineering teams to deliver platform enhancements.
Contribute to solution design, architecture, and data engineering best practices.
Own the end-to-end delivery lifecycle, from development through to production using DevOps and CI/CD practices.
Analyse and resolve data engineering challenges, supporting continuous platform improvement.
Migrate data from legacy systems into Azure-based data lakes and data warehouses.
Develop and maintain PySpark notebooks and data processing solutions.
Improve data quality, reliability, and performance across data pipelines.
Leverage AI-driven tools to enhance coding, testing, and documentation efficiency.
Continuously develop technical expertise and share knowledge across the team.