Lead Data Engineer in London

GLAS is an international provider of institutional debt administration services, serving lenders, borrowers, issuers, and advisers offering a global platform across Loan Agency and related services, Capital Markets and Restructuring.

GLAS’ vision is to be recognised as the best-in-class independent, non-creditor, conflict-free provider of institutional debt administration services, enabling global clients and counterparties to achieve successful outcomes on their transactions.

The business currently comprises c.500 employees who deliver a solution-based, innovative service, ensuring GLAS is the preferred global partner of choice. GLAS has a blue-chip customer base developed over many years; select clients include Apollo, Blackstone, CVC, Deutsche Bank and Goldman Sachs. GLAS has been recognised as the premier independent provider of loan agency and bond trustee services with a portfolio of over $800 billion assets across its global platform. The company is headquartered in London with offices in Paris, Frankfurt, Madrid, New York, New Jersey, Sydney, Melbourne, Brisbane, Singapore, Dubai, Hong Kong, Milan and Rome.

GLAS continues to invest in its data and analytics capabilities to meet the growing scale and complexity of its global operations. As part of this strategic focus, we are hiring a Lead Data Engineer to join our Business Solutions team. This role is instrumental in developing, maintaining, monitoring, and enhancing our Azure Synapse-based enterprise data warehouse—establishing a trusted centre of excellence and a single source of truth for the organisation.

This role will be pivotal in advancing our enterprise data platform, which underpins critical reporting, analytics, and AI-driven insights across the organisation. The ideal candidate will bring a strong blend of technical expertise, architectural vision, and leadership acumen to help us evolve a modern, governed, and scalable data environment that empowers our teams, enhances decision-making, and supports the delivery of exceptional service to our clients worldwide.

The Role

This role encompasses three core areas: Platform Engineering, Data Governance, and Team Leadership. With a strong background in financial services, you will bring proven experience across all three areas, using your deep data engineering expertise to deliver scalable data infrastructure, embed best practices in data quality and governance, and lead a growing team of engineers.

Reporting to the Head of BI and Analytics, this position offers a balance of hands-on technical work and strategic oversight. You will be responsible for the management and optimization of our Azure Synapse Analytics platform, ensuring it serves as a reliable, performant, and secure foundation for our enterprise Data Warehouse. You will lead the design and implementation of robust data pipelines, monitoring frameworks, and automation strategies to ensure timely, accurate, and governed data delivery.

As Lead Data Engineer, you will play a key role in enabling responsible AI and advanced analytics by ensuring our data is AI-ready—clean, validated, and traceable. You will work closely with business stakeholders and system owners across Debt Capital Markets and Loan Administration to ensure data quality is prioritized and embedded into operational processes. You will also identify opportunities to leverage AI and machine learning to automate data engineering workflows, improve anomaly detection, and enhance system resilience.

Beyond technical delivery, this role requires strong leadership to support, mentor, and guide a small team of data engineers. You will foster a culture of innovation, collaboration, and continuous improvement, ensuring the team is aligned with strategic objectives and equipped to deliver high-impact automations and solutions. You will represent the Data Engineering function in cross-functional forums, manage technical backlogs, and ensure delivery is aligned with ISO and governance standards. You will be keen to foster a culture of creativity, collaboration, and continuous learning.

Core Competencies

• Leadership: Proven experience in leading and developing high-performing data engineering teams. Ability to inspire, mentor, and guide junior engineers while fostering a collaborative, agile, and delivery-focused culture.
• Azure Synapse Analytics Expertise: Deep hands-on experience with Azure Synapse, including dedicated SQL pools, serverless architecture, workload management, and performance tuning. Ability to design and maintain scalable, secure, and efficient data warehouse solutions.
• Data Pipeline Engineering: Advanced skills in building and optimizing ETL/ELT pipelines using Azure Data Factory, Python, and SQL. Experience with orchestration, automation, and monitoring of data workflows to ensure reliability and efficiency.
• Data Governance & Quality: Strong understanding of data governance principles, lineage tracking, and quality frameworks. Skilled in implementing validation logic, alerting mechanisms, and monitoring strategies to ensure data accuracy, consistency, and compliance.
• AI-Ready Data Infrastructure: Experience designing structured, validated, and governed data environments to support AI-driven analytics. Proficient in applying AI/ML techniques to enhance data engineering processes, including automated validation, intelligent error handling, and early anomaly detection.
• Programming & Scripting: Strong command of SQL Server, T-SQL, Python with the ability to develop optimised queries and automate complex data workflows across the Microsoft data ecosystem.
• Monitoring & Alerting: Hands-on experience with Azure Monitor for performance tracking, issue diagnosing, and alert configuration within Azure Synapse Analytics to ensure system reliability and proactive incident response.
• Power BI & Reporting Integration: Familiarity with integrating data pipelines into enterprise reporting environments. Experience with Power BI, including data modelling and DAX, to support self-service analytics and ensure alignment between data infrastructure and reporting outputs.
• Cloud & DevOps Practices: Experience with cloud-native data platforms and DevOps practices, including CI/CD pipelines, infrastructure as code, and version control. Familiarity with Azure Monitor, Log Analytics, and other observability tools.
• Agile Delivery: Comfortable working in Agile environments using tools such as Jira and Azure DevOps. Ability to manage backlogs, define user stories, and ensure timely delivery of high-quality solutions.
• Security & Compliance: Understanding of data security best practices, including role-based access control, encryption, and regulatory compliance relevant to financial services.
• Strategic Thinking & Stakeholder Engagement: Ability to align data engineering initiatives with broader business objectives, particularly in financial services contexts such as Debt Capital Markets and Loan Administration. Adept at translating business needs into technical solutions and communicating effectively with both technical and non-technical stakeholders.

Benefits

• Base salary + bonus
• 28 days annual leave + bank holidays
• Long service award
• Private Medical Insurance
• Private pension plan
• Life insurance plan
• Employee Assistance Program (EAP)
• Yearly eye examination
• Gym membership discount
• Career development and study support