Senior Data Engineer (SC Cleared)

LMA Recruitment
London, United Kingdom
Last week
£70,000 – £75,000 pa
Applications closed

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Salary

£70,000 – £75,000 pa

Job Type
Permanent
Work Pattern
Full-time
Work Location
Hybrid
Seniority
Senior
Education
Degree
Security Clearance
Required
Posted
13 Jul 2026 (Last week)

Benefits

25 days holiday + bank holidays Remote working options Professional development support

We are seeking a highly skilled and experienced Senior Data Engineer to join our team and contribute to the development and maintenance of our cutting-edge Azure Databricks platform for economic data. This platform is critical for our Monetary Analysis, Forecasting, and Modelling activities. The Senior Data Engineer will be responsible for building and optimising data pipelines, implementing data transformations, and ensuring data quality and reliability. This role requires a strong understanding of data engineering principles, big data technologies, cloud computing (specifically Azure), and experience working with large datasets.

Essential Skills & Experience:

  • 10+ years of experience in data engineering, with at least 3+ years of hands-on experience with Azure Databricks.
  • Strong proficiency in Python and Spark (PySpark) or Scala.
  • Deep understanding of data warehousing principles, data modelling techniques, and data integration patterns.
  • Extensive experience with Azure data services, including Azure Data Factory, Azure Blob Storage, and Azure SQL
  • Proven expertise with Databricks, including hands-on implementation experience

Key Responsibilities:

  • Data Pipeline Development & Optimisation:
  • Design, develop, and maintain robust and scalable data pipelines for ingesting, transforming, and loading data from various sources (e.g., APIs, databases, financial data providers) into the Azure Databricks platform.
  • Optimise data pipelines for performance, efficiency, and cost-effectiveness.
  • Implement complex data transformations using Spark (PySpark or Scala) and other relevant technologies.
  • Develop and maintain data processing logic for cleaning, enriching, and aggregating data.
  • Ensure data consistency and accuracy throughout the data lifecycle.

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