At a Glance
- Tasks: Lead a high-performing analytics engineering team and deliver top-notch data solutions.
- Company: Join a forward-thinking company redefining travel experiences for millions.
- Benefits: Enjoy mostly remote work, annual pay reviews, and a generous profit-share scheme.
- Other info: Collaborative environment with opportunities for professional growth and development.
- Why this job: Shape the future of analytics engineering and make a real impact.
- Qualifications: Strong background in analytics engineering with advanced SQL skills required.
The predicted salary is between 70000 - 90000 £ per year.
Trusted, high‑quality data is critical to this, and we’re looking for a Lead Data & Analytics Engineer to help drive delivery excellence and build a high‑performing analytics engineering team. This role is analytics engineering led, with responsibility for ensuring the Silver and Gold layers of the platform are delivered to a consistently high standard, are reliable in production, and meet business needs.
You’ll work closely with architects, product and delivery partners, and senior stakeholders, while remaining close enough to the detail to ensure quality, manage risk, and support your team effectively.
Your primary focus is solutions delivery and team effectiveness, ensuring analytics engineering outcomes are delivered predictably, safely, and to a high standard.
- Leading the delivery of analytics‑ready data solutions, with a strong focus on quality, reliability, and fitness for purpose primarily across Silver and Gold data layers.
- Developing and leading a high‑performing analytics engineering team, setting clear expectations around capability, standards, quality, delivery discipline, and professional growth.
- Providing hands‑on technical leadership through risk‑based mentoring, design support, and code reviews, ensuring complex changes are implemented safely and consistently.
- Maintaining a keen eye on detail across analytics transformations, identifying delivery risks early and taking action to address them.
- Ensuring analytics engineering best practices are followed, including testing, documentation, deployment discipline, and operational readiness.
- Working closely with solution and data architects to ensure platform and ingestion decisions support downstream analytics requirements.
- Collaborating with data engineering teams on ingestion and orchestration from a range of sources (databases, flat files, APIs, and event‑driven feeds), while keeping analytics outcomes central.
- Acting as the escalation point for production analytics data assets, supporting issue resolution, root cause analysis, and continuous improvement.
- Supporting recruitment, onboarding, and ongoing development of analytics and data engineers.
- Helping drive a data‑first culture, promoting shared ownership, learning, and continuous improvement across the data community.
Strong background in analytics engineering, with experience delivering complex transformations using SQL‑first approaches, ideally with dbt.
- Delivery‑focused analytics engineering leader who combines technical depth, attention to detail, and people leadership.
- Proven experience understanding, reviewing, and guiding implementation of complex data models across staging (Silver) and warehouse/data‑mart (Gold) layers.
- Advanced SQL capability, with confidence reviewing, optimising, and assuring the quality of complex transformations.
- Experience working with a modern cloud data warehouse, ideally Snowflake, or alternatives such as BigQuery, Redshift, or Synapse.
- Experience working in a cloud environment (AWS, GCP, or Azure), with exposure to services such as cloud storage and orchestration.
- Experience working in an Agile delivery environment (Scrum and/or Kanban), with strong stakeholder communication skills.
- Experience overseeing or supporting data ingestion pipelines, including APIs and event‑driven data sources.
- Experience governing or implementing data CI/CD pipelines (e.g. dbt tests, deployment pipelines, automated checks). We currently use Azure DevOps.
- Working knowledge of Python for analytics engineering or enablement purposes.
- A strong interest in data quality, observability, and operational excellence.
Lead a high‑performing analytics engineering team, not just manage one. Own solutions delivery, balancing pace with quality and risk management. Influence how analytics data is built, trusted, and operated at scale.
AWS, Snowflake, dbt, Airflow, SQL, and Python.
Join us as we redefine travel experiences and create memories for millions of passengers.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Data & Analytics Engineer (Leeds)
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We think you need these skills to ace Lead Data & Analytics Engineer (Leeds)
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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Stand Out with Relevant Courses and Certifications:Although experience talks, relevant courses or certifications can be your ticket to impressing hiring managers at Jet2.com. 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 Jet2.com
✨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!
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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
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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.
