At a Glance
- Tasks: Lead the design and delivery of scalable data platforms and pipelines.
- Company: Join Gymshark, a dynamic company focused on data-driven innovation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on engineering excellence and career progression.
- Why this job: Shape the future of data engineering while mentoring a talented team.
- Qualifications: Strong experience in data engineering and expertise in Google Cloud Platform.
The predicted salary is between 63000 - 77000 £ per year.
This role exists to provide technical leadership within the data engineering team, setting the standard for engineering excellence while remaining deeply hands-on.
The Lead Data Engineer drives the design and delivery of scalable, resilient data platforms and pipelines that underpin Gymshark's ambition to be a truly data driven business.
Sitting at the intersection of craft and collaboration, this role shapes technical direction, raises the capability of those around them, and ensures the team builds the right things in the right way.
WHAT YOU'LL BE DOING
Technical Leadership
- Own and drive the technical design and architecture of data pipelines, data models, and platform components across the team.
- Set and enforce engineering standards: code quality, testing, observability, security, and documentation, ensuring the whole team operates to them
- Lead technical discovery, design sessions, and code reviews, providing constructive, actionable feedback that elevates team output.
- Identify and drive resolution of technical debt, proactively surfacing risks and proposing pragmatic solutions.
- Evaluate emerging tools and technologies (within the GCP ecosystem and beyond) and make grounded recommendations to the Data Engineering Manager.
- Work with the Data Engineering manager to find solutions to complex or ambiguous data engineering problems, unblocking the team and stakeholders.
- Ensure architectural decisions align with Gymshark's data strategy, platform vision, and evolving business needs.
- Champion a culture of engineering excellence through example, documentation, and knowledge sharing.
Delivery
- Own and drive the technical design and architecture of data pipelines, data models, and platform components across the team.
- Set and enforce engineering standards: code quality, testing, observability, security, and documentation, ensuring the whole team operates to them
- Lead technical discovery, design sessions, and code reviews, providing constructive, actionable feedback that elevates team output.
- Identify and drive resolution of technical debt, proactively surfacing risks and proposing pragmatic solutions.
- Evaluate emerging tools and technologies (within the GCP ecosystem and beyond) and make grounded recommendations to the Data Engineering Manager.
- Work with the Data Engineering manager to find solutions to complex or ambiguous data engineering problems, unblocking the team and stakeholders.
- Ensure architectural decisions align with Gymshark's data strategy, platform vision, and evolving business needs.
- Champion a culture of engineering excellence through example, documentation, and knowledge sharing.
People & Craft
- Mentor and coach Data Engineers (Junior through Senior), supporting their technical growth and career progression.
- Facilitate knowledge sharing sessions, pairing, and documentation to build collective capability and reduce knowledge silos.
- Contribute to hiring: lead technical interviews, calibrate assessments, and help onboard new team members effectively.
- Partner with the Data Engineering Manager on team development planning, identifying skill gaps and proposing learning opportunities.
- Ensure team members feel supported, challenged, and set up to do their best work.
Governance & Collaboration
- Partner with Data Governance to embed data quality, access control, and privacy by design into all engineering work.
- Collaborate cross functionally with Data Product, and wider Tech teams to ensure joined up platform delivery.
- Maintain accurate and comprehensive technical documentation: architecture decision records, runbooks, pipeline specs, and data dictionaries.
- Uphold data governance, security, and compliance standards across all data engineering activities.
WHAT YOU'LL NEED
Essential Criteria
- Strong experience in data engineering in a senior or lead-level technical role.
- Deep expertise in Google Cloud Platform: Big Query, Dataflow, Pub/Sub, Cloud Storage, and Cloud Composer (Airflow) as the primary data platform stack.
- Advanced Python and SQL skills, including writing performant, production grade code and conducting rigorous code reviews.
- Proven experience designing and building complex, scalable data pipelines using both batch and streaming/event driven patterns.
- Strong data modelling skills: dimensional modelling, data vault, or equivalent, with a track record of building well structured, reusable Big Query data models.
- Experience with Dataform (or equivalent SQL based transformation tools) for orchestrating transformations within Big Query at scale.
- Solid understanding of software engineering principles: CI/CD, version control (Git), testing frameworks, and infrastructure as code (Terraform).
- Demonstrated ability to embed data quality, observability, and alerting into pipelines (automated validation, anomaly detection, monitoring).
- Experience leading technical design sessions, owning architecture decisions, and communicating trade offs clearly to both technical and non technical audiences.
- Track record of mentoring or coaching engineers and growing technical capability within a team.
- Strong cross functional collaboration and stakeholder management skills, with experience translating business requirements into technical solutions.
Preferred Skills & Experience
- Experience with Data Proc (Spark) for large scale distributed data processing workloads.
- Familiarity with Looker or similar BI tooling, and an understanding of how data models feed downstream analytics and reporting.
- Exposure to analytics engineering practices and tooling (e. g. dbt conceptual patterns, data contracts, semantic layers).
- Experience in e-commerce or retail data environments is desirable.
- Familiarity with data mesh or data platform architecture patterns and their practical application in a scaled organisation.
- Experience with GCP cost management and Big Query cost optimisation strategies.
- Broader exposure to ML infrastructure, feature engineering pipelines, or data science platform enablement.
- #J-18808-Ljbffr
Lead Data Engineer employer: The Inside Job
Gymshark is an exceptional employer that fosters a dynamic and innovative work culture, particularly for the Digital Product Manager role. With a strong emphasis on employee growth and collaboration, team members are encouraged to take ownership of their projects while working alongside talented engineers and designers in a fast-paced environment. Located in a vibrant community, Gymshark offers unique opportunities to engage with a passionate customer base, making it a rewarding place to contribute to meaningful customer experiences.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data Engineer
✨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 The Inside Job!
✨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 Lead Data Engineer at The Inside Job.
✨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 The Inside Job.
✨Apply Directly through Our Website
When you find a suitable opening like Lead Data Engineer at The Inside Job, 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 Lead Data Engineer
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 The Inside Job, 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 The Inside Job. 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 The Inside Job
✨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 The Inside Job!
✨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.
