Data Engineer (Azure)

About the role

We are looking for a Data Engineer to join our team in London and help financial services customers build and operate Azure‑based data platforms. You will design, develop and maintain end‑to‑end data pipelines while ensuring security, performance and compliance in a regulated environment.

Key responsibilities

• Identify and translate customer data‑centric use cases within regulated financial services.
• Design and implement ingestion, processing and transformation pipelines on Azure.
• Build and maintain pipelines for data cleaning, normalisation, enrichment and preparation.
• Apply medallion architecture and appropriate data‑modelling techniques.
• Orchestrate, monitor and optimise Azure Databricks jobs and Azure Data Factory pipelines across dev, UAT and production.
• Configure clusters and compute resources for optimal performance, cost and reliability.
• Use CI/CD pipelines to version and deploy data artefacts and infrastructure.
• Operationalise workflows created by analysts and data scientists.
• Support customers in adopting Azure analytics and machine‑learning services.
• Ensure secure storage, processing and quality of customer data and apply networking and security best practices.
• Design batch and streaming solutions for large‑volume data processing.
• Collaborate with analytics teams on visualisation best practices and reporting enablement.
• Document pipelines, schemas, transformations and operational runbooks.

Required profile

• Positive mindset with enthusiasm for learning new technologies.
• Collaborative, supportive team player with strong ownership and accountability.
• Methodical and analytical approach to problem solving.
• Commitment to data governance, audit readiness and continuous improvement.

Required skills

• Microsoft Azure cloud platform.
• Azure Databricks.
• Azure Data Factory.
• Azure Synapse Analytics.
• Azure Machine Learning (limited usage).
• Azure Document Intelligence.
• Microsoft SQL Server / Azure SQL Database.
• PostgreSQL.
• MySQL.
• CI/CD pipelines for data artefacts and infrastructure.
• Data modelling and medallion architecture.
• Batch and streaming data processing.
• Role‑based access control (RBAC) and data encryption.
• Audit logging and monitoring.