At a Glance
- Tasks: Lead the design and development of cloud-scale data platforms and mentor engineering teams.
- Company: Dynamic tech company focused on innovative data solutions.
- Benefits: Competitive salary, hybrid work model, and opportunities for professional growth.
- Other info: Join a culture of continuous learning and innovation in a fast-paced industry.
- Why this job: Shape the future of data engineering while making a real impact in a collaborative environment.
- Qualifications: Experience in data engineering, Python, AWS, and strong leadership skills.
The predicted salary is between 60000 - 80000 £ per year.
Nottingham/ London (Hybrid) Contract Inside IR35/ Permanent
About the Role
As a Principal Data Engineer, you will combine deep technical expertise with engineering leadership to drive the design, development and evolution of our cloud-scale data platform. You will provide technical leadership across distributed data processing, data products, streaming architectures and data platform capabilities, while remaining hands-on with coding, design and engineering best practices.
You will play a key role in shaping engineering standards, mentoring teams, influencing technical direction and delivering robust, scalable data solutions that enable our Engineers, Data Scientists, Analysts and Governance teams to unlock business value.
What You'll Do
- Lead the design, development and continuous evolution of cloud-scale data platforms and data products across batch and streaming workloads.
- Act as the technical lead for complex data initiatives, providing guidance on solution design, engineering standards and implementation approaches.
- Design, build and optimise distributed data processing pipelines using PySpark, Python, Airflow and AWS services.
- Drive engineering excellence through code reviews, technical coaching, design reviews and adoption of software engineering best practices.
- Partner closely with Product, Architecture, Cyber Security, Data Science, Analytics and Governance teams to deliver scalable and reusable data capabilities.
- Champion modern data engineering patterns including Data Lakehouse architectures, ELT frameworks, Data Products and event-driven processing.
- Influence technology choices, engineering standards and platform roadmaps while collaborating with Architecture and Enterprise Technology teams.
- Improve platform reliability, scalability, observability and operational excellence through automation, monitoring and continuous improvement.
- Drive adoption of CI/CD, Infrastructure-as-Code, testing strategies and engineering quality standards across the Data Engineering function.
- Mentor and develop engineers, fostering a culture of technical excellence, continuous learning and innovation.
- Support the adoption of AI and ML platform capabilities by building trusted, scalable and governed data foundations.
- Contribute hands-on to the delivery of critical solutions, helping teams solve complex technical challenges and accelerate execution.
What You'll Bring
- Significant experience as a Lead Data Engineer, Principal Data Engineer or Technical Lead within a large-scale, data-driven organisation.
- Strong hands-on software engineering expertise with Python, PySpark and Apache Airflow.
- Deep experience designing and building distributed data processing systems and large-scale data platforms on AWS.
- Strong knowledge of modern AWS data technologies including Glue, EMR, Lambda, DynamoDB, S3, EventBridge, Step Functions and related services.
- Proven experience building Data Lakehouse platforms, Data Products and ELT/ETL frameworks supporting analytics, ML and AI workloads.
- Expertise in streaming data architectures, event-driven systems and real-time data processing patterns.
- Strong understanding of data modelling, data quality, metadata management, governance and data platform best practices.
- Experience implementing software engineering best practices including CI/CD, automated testing, infrastructure-as-code and observability.
- Demonstrated ability to lead technical delivery across multiple teams and influence engineering direction without direct authority.
- Excellent stakeholder management and communication skills, with the ability to engage effectively across engineering, product, architecture and senior leadership audiences.
- Practical experience supporting Machine Learning and Generative AI platforms through scalable data engineering solutions.
- Passion for mentoring engineers and building high-performing technical teams.
- Strong problem-solving skills with the ability to balance strategic thinking and hands-on execution.
- Experience operating within regulated industries, ideally Financial Services.
Hurry & apply for a more detailed conversation!
Principal Data Engineer in Nottingham employer: UST
As a Principal Data Engineer at our company, you will thrive in a dynamic and innovative work culture that prioritises technical excellence and continuous learning. With opportunities for mentorship and collaboration across diverse teams, you will play a pivotal role in shaping the future of our cloud-scale data platform while enjoying the flexibility of a hybrid working model in vibrant Nottingham or London. Our commitment to employee growth, coupled with a focus on modern engineering practices, makes us an exceptional employer for those seeking meaningful and rewarding careers in data engineering.
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We think this is how you could land Principal Data Engineer in Nottingham
✨Get Involved in Data Science Meetups
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We think you need these skills to ace Principal Data Engineer in Nottingham
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 UST, 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 UST. 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 UST
✨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 UST!
✨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.
