At a Glance
- Tasks: Lead and scale data engineering while driving innovative data strategies.
- Company: Join a forward-thinking financial services organisation with a collaborative culture.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Dynamic role with a focus on innovation and team development.
- Why this job: Shape the future of data engineering and make a significant impact in finance.
- Qualifications: 5-10 years in Data Engineering with leadership experience required.
The predicted salary is between 80000 - 100000 £ per year.
An innovative financial services organisation is seeking a
Head of Data Engineering to lead and scale its data function.
This is a hybrid leadership and hands‑on technical role, offering the opportunity to shape data strategy, drive engineering excellence, and support business‑critical data initiatives.
- The position combines approximately
- 50% people leadership and
50% hands‑on engineering , requiring a leader who can define and execute a strategic roadmap while remaining technically involved in architecture, solution design, and key engineering initiatives.
- Reporting Structure
- Reports directly to a senior executive leadership team member
- High‑profile position with significant influence across the organisation
- Responsible for hiring, performance management, coaching, and team development
- Team Structure
- Lead a team of 3 Data Engineering professionals
- Planned team growth during the next 12 months
- Responsible for fostering a high‑performance, collaborative engineering culture
Key Responsibilities
- Data Strategy & Leadership
- Define and evolve the organisation's data strategy and roadmap in alignment with business objectives
- Balance short‑term business priorities with long‑term scalable architecture decisions
- Drive adoption of data best practices, governance standards, and engineering principles
- Act as the key stakeholder for data‑related decision making across the organisation
- Team Management
- Lead, mentor, and develop a growing Data Engineering team
- Manage hiring processes, onboarding, coaching, and career development
- Conduct performance reviews and establish effective team operating rhythms
- Create a culture of accountability, collaboration, and continuous improvement
- Hands‑On Data Engineering
- Design, build, and maintain scalable data pipelines and data platforms
- Develop datasets, infrastructure, and internal tooling supporting analytics, research, and product initiatives
- Contribute directly to engineering projects where required
- Make architectural decisions and provide technical leadership across the data estate
- Data Quality & Reliability
- Define and own data quality, availability, coverage, and reliability KPIs
- Implement monitoring, alerting, and observability frameworks Improve resilience, validation processes, and incident management procedures
- Ensure data platforms are scalable, secure, and operationally robust
- Cross‑Functional Collaboration
- Partner closely with engineering, product, analytics, and business stakeholders
- Translate business requirements into scalable data solutions
- Enable data‑driven decision making through robust and accessible datasets
- Align technical priorities with organisational goals
- Engineering Excellence
- Establish standards for testing, code quality, documentation, and deployment practices
- Drive operational excellence and continuous improvement initiatives
- Promote modern software engineering principles across the data team
- Ensure sustainable scaling of both technology and team capabilities
- Desired Skills and Experience
- Leadership Experience
- 5-10+ years of Data Engineering experience
- Minimum 2 years of team leadership or management experience
- Proven track record of building, mentoring, and developing engineering teams
- Experience creating and executing technical roadmaps aligned to business goals
- Technical Expertise
- Strong background designing, building, and operating production‑grade data platforms
- Expertise in data pipeline development, orchestration, monitoring, and operational support
- Experience with orchestration tools such as Apache Airflow or equivalent technologies
- Strong software engineering foundations with a focus on maintainability, scalability, and reliability
- Data & Domain Knowledge
- Experience working with complex, large‑scale datasets
- Exposure to financial services, capital markets, investment management, or similarly data‑intensive environments is highly desirable
- Understanding of market data, reference data, time‑series datasets, or comparable analytical domains
- Technology Stack
Experience with several of the following
- Python
- Apache Spark
- Apache Iceberg
- Postgre SQL
- AWS or equivalent cloud platforms
- Data orchestration and workflow automation technologies
- Monitoring and observability platforms
- Modern data platform architectures
- Professional Skills
- Strong communication and stakeholder management capabilities
- Excellent analytical and problem‑solving skills
- Ability to balance strategic thinking with hands‑on delivery
- Pragmatic approach to engineering trade‑off decisions
- Passion for driving continuous improvement and innovation
- Collaborative leadership style with a commitment to diversity, inclusion, and teamwork
- #J-18808-Ljbffr
Head of Data Engineering employer: Glocomms
At Glocomms, we pride ourselves on being an excellent employer by fostering a dynamic work culture that values innovation and collaboration. Our hybrid work model offers flexibility, allowing employees to balance their professional and personal lives while working on impactful projects for leading insurance clients. With ample opportunities for growth and development, we empower our team members to advance their careers in the exciting field of insurance technology.
StudySmarter Expert Advice🤫
We think this is how you could land Head of Data Engineering
✨Get Involved in Data Science Meetups
Tap into local data science meetups or workshops to connect with fellow enthusiasts and professionals. These events are goldmines for networking, and sometimes even lead directly to job openings at companies like Glocomms!
✨Show Off Your Projects
Start building a public portfolio showcasing your data science projects on platforms like GitHub or personal websites. Highlight unique analyses or models you've developed. This not only demonstrates your skills but also gets your name out there for roles like Head of Data Engineering at Glocomms.
✨Leverage Professional Networks
Join professional bodies related to data science, like the Data Science Society or similar organisations. Getting involved can lead to mentorship opportunities and insider knowledge about full-time positions at companies like Glocomms.
✨Apply Directly through Our Website
When you find a suitable opening like Head of Data Engineering at Glocomms, make sure to apply directly through our website. It gives you an edge and shows you're keen to join our team. Plus, who doesn’t love a direct application? It’s easier than navigating through job boards!
We think you need these skills to ace Head of Data Engineering
Some tips for your application 🫡
Show Off Your Projects:In the world of data science, your projects can speak volumes about your skills. Make sure to showcase a few key projects in your CV or portfolio, especially those that highlight your ability to work with data sets, build models, or use relevant tools like Python, R, or SQL. Don’t forget to include links to any GitHub repositories if applicable!
Quantify Your Achievements:Employers love numbers! When drafting your CV, highlight your achievements with quantifiable results. For instance, mention how your data analysis led to a certain percentage increase in efficiency or revenue at a previous job or project. These details can really make your application pop!
Craft a Tailored Cover Letter:For a full-time role at Glocomms, your cover letter should reflect your passion for data science and your excitement about the specific projects or values of the company. Dive into why you’re a good fit, how your skills align with their needs, and any unique perspectives you can bring to the team.
Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Glocomms. Mention any standout courses you've completed that equipped you with essential skills, such as machine learning certifications or data visualisation courses. This shows your commitment to continuously developing your skills in the field!
How to prepare for a job interview at Glocomms
✨Brush Up on Your Statistics
For a data science role, we need to seriously sharpen our statistics skills. Get ready to tackle technical questions on probability distributions, hypothesis testing, and regression analysis. These are often the bread and butter of data science interviews, so don't just skim over them!
✨Showcase Your Projects
Prepare a killer portfolio showcasing your data science projects. We should include details about the datasets used, the tools and techniques applied, and the impact of your findings. If we can walk them through a particularly challenging project or a cool visualisation that had real-world implications, it’ll really make us stand out!
✨Get Comfortable with Python and R
Most data science positions require us to be proficient in programming languages like Python and R. We should practice common libraries like pandas, NumPy, and scikit-learn, and be ready for live coding exercises or algorithm questions. Showing off our coding chops can really impress the interviewers at Glocomms!
✨Prepare for Case Studies
Expect to encounter real-world case studies during the interview. We might be asked how we’d approach a data problem or analyse a dataset to extract insights. It's essential to think out loud and demonstrate our problem-solving process so that the interviewer can see our logical thinking in action.
