Jet2.com and Jet2holidays
Senior Data & Analytics Engineer
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At Jet2.com and Jet2Holidays, we’re here to deliver amazing journeys – literally. Everything we do is guided by a Customer First mindset, creating unforgettable holidays and flights. None of that happens without great data, and we couldn’t do it without our amazing people.
As a Senior Analytics Engineer, you’ll work as part of a multi‑disciplinary, agile data delivery team, contributing to the build and evolution of analytics‑ready data across our platform. This role is analytics engineering first, with a strong emphasis on implementing and maintaining complex models in our Silver and Gold data layers, rather than defining modelling strategy from scratch.
You’ll join a multi‑disciplinary, agile data delivery team working alongside other analytics and data engineers, data scientists, test engineers, and data visualisation specialists.
What’s in it for you?
- Remote working
- Annual pay reviews
- A generous discretionary profit‑share scheme
- The opportunity to work with a modern data stack and shape analytics at scale
What You’ll Be Doing
As a Senior Analytics Engineer, you’ll focus primarily on the analytics layer of the platform, while working closely with data engineering colleagues on upstream ingestion and orchestration.
Key Responsibilities Include
- Building and maintaining analytics‑ready data models in our cloud data warehouse, transforming raw and curated data into trusted, well‑documented datasets for business and analytical use
- Implementing complex data models and transformations using SQL and dbt, with a strong understanding of how upstream transformations feed downstream analytical use cases
- Working with existing enterprise data models and dimensional structures, confidently navigating and extending them to support new analytics requirements
- Owning and contributing to the enterprise data warehouse, including dimensional models and analytical data sets that serve both technical users and non‑technical business stakeholders
- Collaborating with data engineers on the ingestion and orchestration of data from a wide range of sources (databases, flat files, APIs, and event-driven feeds), ensuring downstream analytics requirements are considered early
- Working closely with analytics, data science, and visualisation teams to ensure data products are fit for purpose, performant, and trusted
- Supporting production data assets, including monitoring, issue resolution, and continuous improvement
- Helping drive a data‑first culture, contributing to data enablement activities, analytics best practices, and knowledge sharing across the data community
- Acting as a senior technical contributor within the team, influencing standards, patterns, and ways of working
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What You’ll Bring
We’re looking for someone who is analytics‑engineering‑led, with enough data engineering experience to work confidently across the full data lifecycle.
Essential Experience
- Strong experience building and maintaining analytics pipelines using SQL‑first transformation patterns, ideally with dbt
- Solid understanding of data warehousing concepts, including how dimensional and analytical models are used downstream, without requiring deep ownership of modelling design decisions
- Advanced SQL skills, with the ability to write, read, and optimise complex queries across large datasets
- Experience working with a cloud data warehouse such as Snowflake (preferred), BigQuery, Redshift, or Synapse
- Experience working in a modern cloud environment (AWS, GCP, or Azure), with exposure to core services such as cloud storage and orchestration
- Experience working in an Agile delivery environment (Scrum and/or Kanban), with strong communication skills and the confidence to work directly with stakeholders at all levels
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Desirable / supporting experience
- Experience contributing to or supporting data ingestion pipelines, including APIs and event‑driven data sources
- Familiarity with orchestration tools (e.g. Airflow) and ELT architectures
- Experience implementing or working with data CI/CD pipelines (for example, dbt tests, deployment pipelines, or automated checks). We currently use Azure DevOps
- Working knowledge of Python for data-related tasks, automation, or light engineering work
- An interest in data quality, observability, and analytics engineering best practices
Why this role is different
This is not a pure platform data engineering role, nor is it a purely reporting-focused analytics role. It’s an opportunity to:
- Own and shape the analytics layer that the business relies on
- Apply modern analytics engineering practices at scale
- Work with a contemporary stack: AWS, Snowflake, dbt, Airflow, SQL, and Python
- Influence how data is modelled, trusted, and used across the organisation
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