About Skyscanner
Everyone loves travelling, but planning is not without its challenges ✈️. That's why we've spent 20 years building tools that turn travel-planning chaos into a breeze. Today, around 100 million travellers count on us every month to skip the whole “47 browser tabs open” phase and find flights, cars, and hotels quickly and easily 💻.

Joining Skyscanner means becoming part of a global brand that's striving to become the planet's go-to travel hack accessible for all 🌍.

Our vision? To be the world's number one travel ally. (Ambitious? 💪 Yes, but, hey, that's what got us here)

About the role

(Hybrid)
Your mission: Join our distributed systems area to design, build and scale the pipelines and platforms that power decision-making across Skyscanner. From analytics to experimentation, your work will help 100 million travellers find their perfect trip — faster, smarter, smoother.

Your impact: You’ll shape the backbone of our Databricks ecosystem, championing modern data engineering practices and raising the bar on data quality, governance and performance. In short: if it moves data, you’ll help make it better.

What you'll be doing

Designing scalable pipelines: Building and maintaining robust Spark/PySpark and SQL pipelines to process large-scale datasets reliably and efficiently.
Orchestrating with confidence: Creating and managing data workflows that keep our data fresh, trusted and right where it needs to be.
Powering our Lakehouse: Implementing and optimising data solutions on Databricks, leveraging Delta Lake and modern Lakehouse architecture.
Structuring data the smart way: Applying Medallion Architecture principles (Bronze, Silver, Gold) to transform raw data into high-quality, analytics-ready assets.
Championing governance: Using Unity Catalog to enable access control, lineage tracking and metadata management — because great data needs great guardrails.
Elevating quality and performance: Establishing data quality monitoring (Monte Carlo or similar), optimising pipelines for performance and cost, documenting architecture, and contributing to code reviews and engineering best practices.

About you

Experienced in data engineering: You bring 3+ years of experience building and maintaining production-grade data pipelines and infrastructure.
Fluent in Python and SQL: You’re confident writing efficient, maintainable code for large-scale data processing.
Spark-savvy: Hands-on experience with Apache Spark and PySpark is second nature to you.
Airflow-ready: You’ve built and supported reliable production workflows using Apache Airflow.
Lakehouse literate: You’ve worked with Databricks and Delta Lake, and understand Medallion Architecture patterns.
Governance and modelling minded: You’re comfortable with data modelling concepts and understand governance principles such as Unity Catalog.
Quality-driven and curious: You’ve used data quality monitoring tools (Monte Carlo or similar), communicate clearly across teams, and ideally bring bonus experience with Lakeflow/Delta Live Tables, CI/CD, AWS, infrastructure as code, streaming data, distributed systems or Databricks certifications.

What it's like here

We are the real deal— no corporate gloss, no empty promises. Just a team of genuinely curious, caring humans ❤️, building things that help travellers explore the world a little easier 🧭.

Skyscanner is made up of brilliant humans from every corner of the world. We believe travel makes the world better — and that the same is true of our diverse teams. We're proud to be an equal opportunities employer and are committed to building an inclusive workplace where everyone can thrive and products that are accessible to all ✨.

Sound like your kind of adventure? 🚀 Apply now and help us shape the future of travel.

We're committed to ensuring our application and recruitment processes are inclusive and accessible to everyone. If you require any reasonable adjustments or accommodations for interviews, and/or wish to apply under the Disability Confident scheme, please let your recruiter know. If you’d like more information on any of our policies, such as hybrid working or Parental Leave policies (typically we pay a minimum of 24 weeks birth parent/maternity leave globally), our recruitment team can provide more information on these.

Data Engineer

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