Senior Data Engineer

We're looking for a Senior Data Engineer to own our data assets after initial ingestion - taking raw data through to validated, business-ready layers in Snowflake that our data science team relies on every day. This is a hands-on role with real ownership: you'll be the person we trust to keep data flowing, correct and well documented. What you'll do Own the data lifecycle after ingestion: transform raw (base) data into clean silver and business-ready gold layers in Snowflake Design, build, and maintain pipelines and orchestration in Airflow Write clean, performant SQL and Python transformations Manage and improve our data infrastructure across AWS and Snowflake Monitor data quality - validation checks, alerting and fixing issues before anyone else notices them Work hand-in-hand with data scientists: prepare features, training datasets and model-ready data you understand well enough to challenge Document your work clearly so anyone on the team can pick it up What we're looking for Communication comes first. Excellent written and spoken English is essential for this role - not a nice-to-have. You'll explain technical decisions to non-technical colleagues, write documentation others depend on and communicate clearly and early when something goes wrong. If you're a brilliant engineer who can't explain your work, this isn't the role for you. Strong data science skills are just as important. This is not a pure pipelines role. You'll work directly with our data science team, and you need to genuinely understand their world: feature engineering, preparing and validating training data, statistics and what "good" data looks like for machine learning. You should be able to read, discuss and question data science code - not just feed it data. Technical: Strong SQL and Python - the core of your day-to-day Solid production experience with Snowflake - data modelling, performance tuning, and warehouse/cost management Solid production experience with Airflow Hands-on experience with the AWS data stack Experience with layered data architectures (base/bronze → silver → gold) Solid data science foundation - feature engineering, statistics, and preparing data for machine learning models How you work: Responsible and reliable - you take ownership end to end, and people can depend on you without chasing Dynamic - comfortable in a fast-moving environment, happy to context-switch and pick up new tools quickly How to apply In your proposal, include a short written introduction telling us about a data pipeline or platform you've personally owned - what it did, what broke and how you fixed it. If it fed a machine learning model, even better. We take communication seriously, and your introduction is the first thing we'll read.