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Data Engineer

Peregrine
London, United Kingdom
2 months ago
Applications closed

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Posted
6 Mar 2026 (2 months ago)
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We are Data Services, our mission is to unlock the value of data by delivering high-quality, reliable, and secure data services that are accessible, understandable, and actionable. We continuously evolve our offerings, leveraging modern cloud-based technologies, and fostering strong partnerships to help our colleagues in the Bank navigate the complexities of a data-driven world and achieve their strategic objectives.
Active SC Clearance
Job Description:
The world of data in Central Banking is evolving rapidly. With the rise of detailed data collection in financial regulation and the swift advancements in cloud-native data technologies, the demand for visionary data engineers is growing. We’re seeking a senior Data Engineer to join our Data Engineering team and play a pivotal role in shaping the Bank’s strategic cloud-first data platform.
As a senior member of the team, you will play a key role in designing and delivering robust, scalable data solutions that support the Bank’s core responsibilities around monetary policy, financial stability, and regulatory supervision. You’ll contribute to technical design decisions, mentor engineers, and collaborate across teams to ensure our data infrastructure continues to evolve and meet future demands.
Role Responsibilities

  • Lead the design, development, and deployment of scalable, secure, and cost-effective distributed data solutions using Azure services (e.g., Azure Databricks, Azure Data Lake Storage, Azure Data Factory).
  • Architect and implement advanced data pipelines using Databricks, Delta Lake, Python and Spark, ensuring performance, reliability, and maintainability across cloud and on-prem environments.
  • Champion data quality, governance, and observability, ensuring data is accurate, timely, and fit-for-purpose for analytics, BI, and operational use cases.
  • Drive the modernization of legacy systems, leading the migration of data infrastructure to Azure with minimal disruption and long-term scalability.
  • Act as a technical authority on Azure-native data engineering, guiding best practices and setting standards across the team.
  • Mentor and coach junior and mid-level engineers, fostering a culture of continuous learning, innovation, and technical excellence.
  • Collaborate with architects, analysts, and stake holders to align data engineering efforts with strategic business goals and enterprise data strategy.
  • Evaluate and introduce emerging technologies, tools, and methodologies to enhance the Bank’s data capabilities.
  • Own the end-to-end delivery of complex data solutions, from requirements gathering to production deployment and support.
  • Contribute to the development of reusable frameworks, templates, and patterns to accelerate delivery and ensure consistency across projects.
    Minimum Criteria
  • Extensive experience with Azure services including Azure Databricks, Azure Data Lake Storage, and Azure Data Factory.
  • Advanced proficiency in SQL, Python, and Spark (PySpark), with a strong focus on performance optimization and distributed processing.
  • Proven experience in CI/CD practices using industry-standard tools (e.g., GitHub Actions, Azure DevOps).
  • Strong understanding of data architecture principles and cloud-native design patterns.
    Essential Criteria
  • Demonstrated ability to lead technical delivery, mentor engineering teams and collaborate with stakeholders to ensure alignment between data solutions and business strategy.
  • Proficiency in Linux/Unix environments and shell scripting.
  • Deep understanding of source control, testing strategies, and agile development practices.
  • Self-motivated with a strategic mindset and a passion for driving innovation in data engineering.
    Desirable Criteria
  • Experience delivering data pipelines on Hortonworks/Cloudera on-prem and leading cloud migration initiatives.
  • Familiarity with: Apache Airflow
  • Data modelling and metadata management
  • Experience influencing enterprise data strategy and contributing to architectural governance

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