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- Tasks: Lead the design and build of scalable data solutions while mentoring a team.
- Company: Join a global leader in personalised photo products, shaping customer experiences.
- Benefits: Enjoy a competitive salary, bonus, and a flexible work environment in Central London.
- Why this job: Be at the forefront of data innovation in a supportive, purpose-driven culture.
- Qualifications: Strong background in data engineering, proficient in Python, SQL, and cloud services.
- Other info: Opportunity to influence architecture and drive decentralised data models.
The predicted salary is between 58200 - 117400 £ per year.
Salary: £97,000 + 15% bonus
Location: Central London, 2 days in office
We're hiring on behalf of our client, a global leader in personalised photo products, for an experienced Principal Data Engineer to join their UK data & ML team. This is a senior hands-on leadership role driving data platform strategy and engineering standards as they evolve toward decentralised data and ML adoption.
Role overview:
You'll play a central role in re-architecting and scaling their data platform to meet growing business and customer needs. This includes building robust, observable data pipelines, ensuring data trustworthiness, and mentoring a team of engineers while collaborating closely with Product, Ops, and Marketing stakeholders.
Key responsibilities:
- Lead design and build of scalable, cloud-native data solutions with best-in-class governance and observability
- Define technical principles and data engineering standards across distributed teams
- Coach data and analytics engineers on SDLC best practices (CI/CD, testing, versioning)
- Contribute to strategic planning and technical roadmaps in collaboration with product and engineering leads
- Influence cross-functional stakeholders on architecture and implementation trade-offs
- Ensure data is reliable, timely, and actionable for operational and ML-driven use cases
About you:
- Strong background in software and data engineering leadership
- Proficient in Python, SQL, and modern ELT practices (e.g. dbt, Fivetran, Airflow)
- Deep knowledge of data warehousing (Snowflake), AWS services (e.g. Lambda, Kinesis, S3), and IaC (Terraform)
- Experienced in building data platforms with a focus on governance, reliability, and business value
- Comfortable driving architectural conversations and mentoring engineers across disciplines
- Advocate for decentralised data models, such as data mesh
Nice to have:
- Experience with data quality tools (e.g., Monte Carlo)
- Knowledge of data security and compliance
- Previous work in e-commerce or consumer tech
This is a chance to shape the next generation of data systems powering personalised customer experiences at scale - while working in a people-first, purpose-driven culture.
Get in touch to find out more or apply.
Principal Data Engineer employer: Harnham - Data & Analytics Recruitment
Contact Detail:
Harnham - Data & Analytics Recruitment Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Principal Data Engineer
✨Tip Number 1
Familiarise yourself with the latest trends in data engineering, especially around decentralised data models like data mesh. This knowledge will not only help you during interviews but also demonstrate your commitment to staying ahead in the field.
✨Tip Number 2
Network with professionals in the data engineering space, particularly those who have experience with AWS services and data warehousing solutions like Snowflake. Engaging in conversations can provide insights into the role and may even lead to referrals.
✨Tip Number 3
Prepare to discuss your leadership style and experiences in mentoring engineers. Highlight specific examples where you've influenced architectural decisions or improved team practices, as this is crucial for a Principal Data Engineer role.
✨Tip Number 4
Showcase your hands-on experience with tools like dbt, Fivetran, and Airflow during discussions. Being able to speak confidently about your practical knowledge will set you apart from other candidates.
We think you need these skills to ace Principal Data Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights your experience in data engineering, particularly with Python, SQL, and cloud services like AWS. Emphasise any leadership roles you've held and your ability to mentor others.
Craft a Compelling Cover Letter: In your cover letter, explain why you're passionate about data engineering and how your skills align with the company's goals. Mention your experience with decentralised data models and your approach to building scalable data solutions.
Showcase Relevant Projects: Include specific examples of projects where you've led the design and implementation of data platforms. Highlight your contributions to governance, reliability, and observability in these projects.
Prepare for Technical Questions: Be ready to discuss your technical expertise in detail. Prepare to answer questions about your experience with data warehousing, ELT practices, and architectural decisions you've made in previous roles.
How to prepare for a job interview at Harnham - Data & Analytics Recruitment
✨Showcase Your Technical Expertise
Be prepared to discuss your experience with Python, SQL, and modern ELT practices. Highlight specific projects where you've built scalable data solutions, and be ready to explain the technical decisions you made and their impact on the business.
✨Demonstrate Leadership Skills
As a Principal Data Engineer, you'll need to lead and mentor others. Share examples of how you've coached teams in best practices for software development life cycles (SDLC) and how you've influenced architectural decisions across teams.
✨Understand the Business Context
Familiarise yourself with the company's products and how data plays a role in enhancing customer experiences. Be ready to discuss how your work can drive business value and support operational and ML-driven use cases.
✨Prepare for Architectural Discussions
Expect to engage in conversations about architecture and implementation trade-offs. Brush up on decentralised data models like data mesh and be ready to articulate your thoughts on governance, reliability, and observability in data platforms.
