At a Glance
- Tasks: Lead data engineering efforts to create reliable datasets for analytics and AI-driven insights.
- Company: Join THG, a global leader in ecommerce with a focus on beauty and sports nutrition.
- Benefits: Enjoy competitive salary, enhanced leave, wellbeing support, and career development opportunities.
- Other info: Work remotely and be part of a diverse, inclusive team committed to excellence.
- Why this job: Shape the future of data engineering and make a real impact in a dynamic environment.
- Qualifications: Strong Python skills and experience in building production data pipelines required.
The predicted salary is between 75600 - 92400 £ per year.
About THG
We are THG, a global ecommerce group on a mission to be the global online leader in beauty and sports nutrition. Our portfolio of leading retailers and brands such as LOOKFANTASTIC, Myprotein, ESPA, Perricone MD, and Cult Beauty form our two core businesses: ensuring everyone is paid on time, that materials are sourced and arrive promptly, that we meet our legal obligations and that our health, safety, and security is safeguarded.
Why be the Data Engineering Lead at THG?
THG is building the data engineering capability that will underpin everything from daily trading analytics to AI-driven personalisation. As Engineering Lead, you will own the technical vision and delivery for how THG structures, stores and distributes its data across three core domains: Customer, Commercial and Operations.
Your primary mission is to move THG from a fragmented, siloed data landscape towards consistent, joined-up domain datasets that can be trusted and used at scale. You will build the engineering foundation that changes that — working to a shared architecture that ensures Customer, Commercial and Operations datasets have consistent standards, shared attributes where appropriate, and can ultimately be joined into a single, reliable view of the business.
Your job is to take what Ingenuity provides and shape it into the structured, high-quality domain datasets that analytics and data science teams can confidently build on. You will also be a key partner to the Data Governance Lead, implementing the standards they define within your engineering practice.
Technical Leadership & Architecture
- Own the data engineering architecture for THG — defining how data is ingested, transformed, stored and made available across Customer, Commercial and Operations domains.
- Design consistent standards and patterns across all three domains — ensuring shared attributes are handled uniformly and that data can be reliably joined across domains.
- Lead the evolution of our BigQuery/GCP platform — driving migrations, modernising legacy pipelines, and building for scalability.
- Make key architectural decisions and be accountable for the technical quality of everything the engineering team produces.
- Develop and enforce engineering standards across the team — version control, testing frameworks, error handling, documentation and code review practices.
Domain Dataset Development
- Lead the build-out of structured, reliable domain datasets — moving from disparate, siloed data sources towards consolidated Customer, Commercial and Operations data platforms.
- Work towards the foundations of a single source of truth — defining how data from different source systems is reconciled, deduplicated and mastered within each domain.
- Ensure domain datasets are structured for downstream consumption — analytics teams, data science models, BI tooling and AI/agent use cases should all be able to reliably consume what engineering produces.
AI-Enabled Engineering
- Champion the use of AI in data engineering — this is a small team with a large remit, and we expect AI-assisted development to be embedded in how we work.
- Apply AI tools to accelerate pipeline development, automated testing, anomaly detection and data quality monitoring.
- Stay current with AI developments in the data engineering space and bring new approaches into the team's practice.
Ingenuity Relationship
- Work closely with the Ingenuity engineering team — understanding what they provide at the platform layer and building effectively on top of it.
- Define clear requirements and interface standards for how Ingenuity data feeds into PLC domain datasets.
- Identify and escalate where Ingen platform behaviour affects data quality or availability within PLC domains.
People & Team
- Manage, coach and develop a team of six domain-aligned engineers — three Senior Data Engineers and three Data Engineers.
- Run a structured review and development process for each engineer, building their technical capability and career progression.
- Foster a culture of engineering excellence, collaboration and continuous improvement.
What skills and experience do I need for this role?
- You must have built and operated production data pipelines on this stack.
- Strong Python skills — used daily for pipeline development, data transformation and automation.
- Experience across at least two of the three core data domains: Customer, Commercial and Operations.
- A track record of evolving data architecture — you can demonstrate concrete examples of how you changed the way a business structures, stores or consumes its data.
- Deep understanding of data modelling, pipeline design and distributed data architecture.
- Experience with Dataform or dbt for data transformation.
- Demonstrated use of AI in data engineering — AI-assisted pipeline development, automated quality checks, or similar practical applications.
- Experience mentoring and developing engineers.
Desirable
- Experience building towards a Customer Data Platform (CDP) or equivalent customer data consolidation project.
- Familiarity with data governance principles and working alongside a governance function.
- E-commerce or retail background.
- Experience with streaming or real-time data pipelines.
What’s in it for me?
- Career Development: Access bespoke development programmes that have been designed and developed by our in-house L&D team. Continued development through our upskilling programme that is delivered in partnership with an industry-leading training provider.
- Enhanced Leave: 25 days annual leave plus bank holidays. Enhanced maternity and paternity pay, depending on length of service.
- Wellbeing Support: Access face-to-face and virtual appointments with our in-house GP. Access our 247 Employee Assistance Programme (EAP) which is provided by Bupa. State-of-the-art on-site gym.
- Other Perks: Save up to 12% on the cost of personal tech through our salary sacrifice scheme. Subsidised bus pass from Manchester City Centre to our ICON office. Up to 50% staff discount on THG brands. On-site staff shop. Refer them and get up to 1000 when they pass their probation. Anniversary gifts when you hit 5 and 10 years of service.
THG is proud to be a Disability Confident Committed employer. THG is committed to creating a diverse & inclusive environment and hence welcomes applications from all sections of the community.
If you haven't heard from us within that time frame or should you have any specific questions about this or other applications for positions at THG please contact one of our Talent team to discuss further.
Data Engineering Lead - REMOTE in Manchester employer: The Hut Group
At Lookfantastic, we pride ourselves on being a dynamic and innovative employer that champions creativity and collaboration in the heart of Manchester. Our commitment to employee growth is reflected in our bespoke development programmes and upskilling initiatives, ensuring that you can thrive in your role as Editorial & Content Lead while enjoying a vibrant work culture that values diversity and inclusivity. With competitive benefits like enhanced leave, access to wellness resources, and generous staff discounts, we offer a rewarding environment where you can make a meaningful impact in the beauty industry.
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We think you need these skills to ace Data Engineering Lead - REMOTE in Manchester
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!
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✨Brush Up on Your Statistics
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✨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.
