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?
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Remote working
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Annual pay reviews
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A generous discretionary profit‑share scheme
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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:
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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
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Implementing complex data models and transformations using SQL and dbt, with a strong understanding of how upstream transformations feed downstream analytical use cases
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Working with existing enterprise data models and dimensional structures, confidently navigating and extending them to support new analytics requirements
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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
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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
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Working closely with analytics, data science, and visualisation teams to ensure data products are fit for purpose, performant, and trusted
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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
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
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Strong experience building and maintaining analytics pipelines using SQL‑first transformation patterns, ideally with dbt
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Solid understanding of data warehousing concepts, including how dimensional and analytical models are used downstream, without requiring deep ownership of modelling design decisions
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Advanced SQL skills, with the ability to write, read, and optimise complex queries across large datasets
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Experience working with a cloud data warehouse such as Snowflake (preferred), BigQuery, Redshift, or Synapse
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Experience working in a modern cloud environment (AWS, GCP, or Azure), with exposure to core services such as cloud storage and orchestration
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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
Desirable / supporting experience
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Experience contributing to or supporting data ingestion pipelines, including APIs and event‑driven data sources
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Familiarity with orchestration tools (e.g. Airflow) and ELT architectures
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Experience implementing or working with data CI/CD pipelines (for example, dbt tests, deployment pipelines, or automated checks). We currently use Azure DevOps
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Working knowledge of Python for data-related tasks, automation, or light engineering work
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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:
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Own and shape the analytics layer that the business relies on
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Apply modern analytics engineering practices at scale
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Work with a contemporary stack: AWS, Snowflake, dbt, Airflow, SQL, and Python
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Influence how data is modelled, trusted, and used across the organisation
