At a Glance
- Tasks: Lead the evolution of our Azure data platform and build scalable data pipelines.
- Company: Join GLAS, a top global provider in institutional debt administration services.
- Benefits: Competitive salary, 28 days leave, private medical insurance, and gym discounts.
- Other info: Dynamic hybrid work environment with strong career development opportunities.
- Why this job: Shape a modern data platform and lead a high-impact engineering team.
- Qualifications: Proven experience in data engineering, especially with Azure and financial services.
The predicted salary is between 70000 - 90000 € per year.
About GLAS
GLAS is a leading global provider of institutional debt administration services, supporting lenders, borrowers, issuers, and advisers across Loan Agency, Capital Markets, and Restructuring.
Location: London (Hybrid) | Salary: Competitive + bonus
With c.500 employees across major financial centres—including London, New York, Paris, Frankfurt, Singapore, and Sydney—GLAS delivers innovative, solution‑led services to a blue‑chip client base including Apollo, Blackstone, CVC, Deutsche Bank, and Goldman Sachs. Our vision is simple: to be the best‑in‑class independent, conflict‑free partner for institutional debt administration—helping clients achieve successful outcomes on complex transactions.
The Opportunity
As part of our continued investment in data and analytics, we are hiring a Lead Data Engineer to join our Business Solutions team. This is a pivotal role responsible for evolving our Azure Synapse‑based enterprise data platform into a scalable, governed, and high‑performing “single source of truth” across the organisation. You’ll combine hands‑on engineering expertise with strategic leadership, driving the development of robust data pipelines, advancing data governance, and enabling AI‑ready data capabilities.
What You’ll Be Doing
- Platform & Data Engineering
- Own and optimise our Azure Synapse Analytics platform.
- Design and build scalable ETL/ELT pipelines using Azure Data Factory, SQL, and Python.
- Implement monitoring, alerting, and automation to ensure data reliability and performance.
- Deliver a secure, high‑quality enterprise data warehouse supporting critical reporting and analytics.
- Data Governance & AI Enablement
- Establish and embed data quality, lineage, and governance frameworks.
- Ensure data is clean, validated, and AI‑ready.
- Drive initiatives leveraging AI/ML to improve data engineering workflows (e.g., anomaly detection, automation).
- Collaborate with stakeholders across Debt Capital Markets and Loan Administration to improve data integrity.
- Leadership & Delivery
- Lead, mentor, and develop a growing team of data engineers.
- Foster a culture of innovation, collaboration, and continuous improvement.
- Own technical backlogs and delivery through Agile frameworks.
- Represent Data Engineering across cross‑functional initiatives and governance forums.
What We’re Looking For
- Experience & Technical Expertise
- Proven experience in a Lead/Senior Data Engineering role, ideally within financial services.
- Deep expertise in Azure Synapse Analytics (dedicated SQL pools, performance tuning, architecture).
- Strong experience building data pipelines with Azure Data Factory, Python, and SQL.
- Advanced SQL/T‑SQL skills and strong scripting ability.
- Data & Platform Capability
- Strong understanding of data governance, quality frameworks, and lineage tracking.
- Experience building AI‑ready data environments.
- Hands‑on experience with monitoring and observability tools (e.g., Azure Monitor, Log Analytics).
- Exposure to Power BI and data modelling concepts.
- Leadership & Delivery
- Experience leading and mentoring engineers within agile environments.
- Strong stakeholder management and ability to translate business needs into technical solutions.
- Knowledge of DevOps practices (CI/CD, IaC, version control).
- Security & Domain
- Understanding of data security, access controls, and regulatory requirements.
- Financial services experience (especially Debt Capital Markets or Loan Administration) is highly desirable.
Why Join GLAS?
- Play a key role in shaping a modern enterprise data platform.
- Work with a global, high‑calibre client base.
- Be part of a business investing heavily in data, analytics, and AI.
- Lead and grow a high‑impact engineering team.
Benefits
- Competitive base salary + bonus.
- 28 days annual leave + bank holidays.
- Private medical insurance & pension.
- Life insurance.
- Employee Assistance Programme (EAP).
- Eye care support.
- Gym membership discounts.
- Ongoing career development and study support.
Lead Data Engineer employer: GLAS
GLAS is an exceptional employer, offering a dynamic work environment in the heart of London where innovation and collaboration thrive. With a strong commitment to employee growth, GLAS provides extensive career development opportunities, competitive salaries, and a comprehensive benefits package, including private medical insurance and gym membership discounts. Join a forward-thinking team that values your expertise and empowers you to shape the future of data engineering in a global financial services context.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer
✨Tip Number 1
Network like a pro! Reach out to connections in the industry, attend meetups, and engage on platforms like LinkedIn. We can’t stress enough how personal connections can open doors that applications alone can’t.
✨Tip Number 2
Prepare for interviews by researching GLAS and its projects. Understand their data platform and think about how your skills can contribute. We want you to shine and show them you’re the perfect fit for the Lead Data Engineer role!
✨Tip Number 3
Practice your technical skills! Brush up on Azure Synapse, SQL, and Python. We recommend doing mock interviews with friends or using online platforms to get comfortable discussing your expertise.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Lead Data Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV is tailored to the Lead Data Engineer role. Highlight your experience with Azure Synapse Analytics, data pipelines, and any leadership roles you've held. We want to see how your skills match what we're looking for!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about data engineering and how you can contribute to our team at GLAS. Be sure to mention specific projects or achievements that relate to the job description.
Showcase Your Technical Skills:In your application, don't forget to showcase your technical expertise. Mention your experience with Azure Data Factory, SQL, and Python, and any relevant tools you've used. We love seeing candidates who are hands-on and ready to dive into our tech stack!
Apply Through Our Website:We encourage you to apply through our website for the best chance of getting noticed. It helps us keep track of applications and ensures you’re considered for the role. Plus, it’s super easy to do!
How to prepare for a job interview at GLAS
✨Know Your Tech Inside Out
Make sure you’re well-versed in Azure Synapse Analytics, Azure Data Factory, and the programming languages mentioned in the job description. Brush up on your SQL and Python skills, and be ready to discuss specific projects where you've implemented these technologies.
✨Showcase Your Leadership Skills
Since this role involves leading a team, prepare examples of how you've mentored or managed engineers in the past. Think about times when you fostered collaboration or drove innovation within your team, as these experiences will resonate well with the interviewers.
✨Understand Data Governance
Familiarise yourself with data governance frameworks and be prepared to discuss how you’ve ensured data quality and lineage in previous roles. Highlight any experience you have with AI-ready data environments, as this is crucial for the position.
✨Prepare for Scenario-Based Questions
Expect questions that assess your problem-solving abilities and technical expertise. Practice articulating your thought process when faced with challenges related to data pipelines, performance tuning, or stakeholder management. This will demonstrate your analytical skills and strategic thinking.
