At a Glance
- Tasks: Lead a team of data engineers to deliver high-quality data solutions.
- Company: Join Hargreaves Lansdown, a forward-thinking financial services company.
- Benefits: Enjoy flexible working, generous holiday, and comprehensive health benefits.
- Other info: Inclusive workplace that values diversity and offers excellent career growth.
- Why this job: Make a real impact in a regulated environment while growing your leadership skills.
- Qualifications: 8+ years in data engineering with proven people management experience.
The predicted salary is between 70000 - 90000 £ per year.
Excited to grow your career? Our purpose is to make it easy for people to save and invest for a better future. We are looking for great people to join us, so please come and invest in YOUR future at Hargreaves Lansdown. We know that sometimes people can be put off applying for a job if they don't tick every box. If you're excited about working for us and have most of the skills or experience we're looking for, please go ahead and apply. We’d love to hear from you!
About the role
Someone needs to make sure the engineering actually ships, reliably, at scale, and to a standard that holds up under regulatory scrutiny. That's this role. You'll lead a team of data engineers, owning delivery end‑to‑end: pipelines, transformations, platform components, production operations, and the engineering practices that tie it all together. You'll set the bar for quality and ownership, manage cost and performance, and build a team culture where things are done properly, not just done. This is hands‑on leadership. You won't be writing every pipeline, but you'll know what good looks like, hold people to it, and clear the path so they can deliver. You'll operate in a regulated financial services environment where auditability, resilience, and governance aren't optional extras, they're the baseline.
What you'll be doing:
- Leading a team of data engineers - hiring, coaching, developing, and setting clear expectations for delivery quality and ownership
- Owning end‑to‑end engineering delivery against the data product roadmap: pipelines that are idempotent, tested, observable, and documented
- Running production operations - monitoring, incident response, root‑cause analysis, and ensuring issues are resolved with clear ownership and learning
- Defining and enforcing engineering standards across the team: coding conventions, testing strategy, CI/CD, code review, and documentation
- Owning the cost and performance profile of data infrastructure - actively optimising compute, storage, and resource utilisation
- Managing technical debt as a visible backlog item, not an invisible tax on delivery speed
- Partnering with Data Product Managers on priorities and trade‑offs, and with the Principal Data Modeller on data model standards
- Ensuring engineering delivery meets regulatory, security, and governance requirements - auditable, recoverable, and secure by default
About you:
- 8+ years in data engineering or software engineering, with at least 2–3 years in people management
- Proven experience delivering and operating production data platforms and pipelines at scale
- Experience defining and enforcing engineering standards across a team
- Strong operational mindset: reliability, monitoring, incident response, cost management
- Hands‑on with modern cloud data platforms (e.g. Snowflake, BigQuery, Redshift)
- Experience with orchestration tools (e.g. Airflow, Dagster), CI/CD, and automated deployment
- Confident influencing stakeholders and making delivery trade‑offs with transparency
- Comfortable delegating - accountable for outcomes, not personal code output
- Demonstrated ability to build, grow, and retain high‑performing engineering teams
- Experience in a regulated environment (financial services, insurance, or banking)
- Experience operating within a data product or platform operating model, not solely project‑based delivery
Desirable:
- Experience with transformation frameworks (e.g. dbt)
- Experience with streaming or event‑driven architectures
- Exposure to semantic layers, metrics layers, or feature engineering patterns
- Experience managing platform costs and optimising spend at scale
- Familiarity with data governance tooling (catalogues, lineage tools, quality frameworks)
- Experience supporting AI/ML feature pipelines or model serving infrastructure
What's on offer?
- Discretionary annual bonus and annual pay review
- 25 days holiday plus bank holidays and 1‑day additional Christmas closure
- Option to purchase an additional 5 days holiday
- Flexible working options available, including hybrid working
- Enhanced parental leave
- Pension scheme up to 11% employer contribution
- Income protection and life insurance (4 x salary core level of cover)
- Private medical insurance
- Health care cash plans - including optical, dental, and out patient care
- Health screening programme
- Help@hand - confidential support including mental health counselling and remote GP
- Wellhub - unlimited access to fitness provider and wellness coach sessions
- Variety of travel to work schemes with bike storage and shower facilities
- Inhouse barista and deli serving subsidised coffee and sandwiches
- Two paid volunteering days per year
Hargreaves Lansdown is an inclusive employer that values diversity in its workforce. We encourage applications from all individuals without regard to race, religion, gender, sexual orientation, national origin, disability or age. This role may also be available on a flexible working or part time basis – please ask the Recruitment & Onboarding team for more information. Please note, we are unable to provide employment sponsorship to candidates.
Data Engineering Manager in Bristol employer: SwiftCruit
At Hargreaves Lansdown, we are committed to fostering a dynamic and inclusive work environment where your career can flourish. As a Data Engineering Manager, you will lead a talented team in a regulated financial services setting, benefiting from flexible working options, generous holiday allowances, and comprehensive health and wellness programmes. Join us to not only advance your professional journey but also to contribute to our mission of empowering individuals to save and invest for a better future.
StudySmarter Expert Advice🤫
We think this is how you could land Data Engineering Manager in Bristol
✨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 SwiftCruit!
✨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 Data Engineering Manager at SwiftCruit.
✨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 SwiftCruit.
✨Apply Directly through Our Website
When you find a suitable opening like Data Engineering Manager at SwiftCruit, 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 Data Engineering Manager in Bristol
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 SwiftCruit, 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 SwiftCruit. 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 SwiftCruit
✨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 SwiftCruit!
✨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.