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Analytics Engineer (m/f/d)

appinio · London, United Kingdom

Confidence
Medium confidence
Posted
24 Oct 2026
First seen
24 Oct 2026
Last checked

Evidence

Medium confidence

appinio is on the UK Home Office register. The posting doesn’t mention visas.

Sponsor register

UK Home Office Register of Licensed Sponsors

Listed as Appinio Limited. London · Skilled Worker route · A rating. Snapshot 26 Oct 2026.

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Job posting

Doesn’t mention visas or relocation.

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Seen on the job board

First seen 24 Oct 2026. We check every day and remove closed roles.

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Being on the register means appinio can sponsor visas. It doesn’t confirm this role qualifies, so check with the recruiter.

About the role

Allow us to introduce ourselves Hello there! We’re Appinio, a global market research company that combines an AI-powered insights platform with expert consultancy. On a mission to make the world a little more fact-based, we help companies understand how consumers think and make smarter decisions. We started out in Hamburg back in 2014, but we’re now humbled to call the world our playground, with over 260 employees spread across 13 countries (we’re remote-first), 2600+ international clients, delivering research from +190 markets, globally.

Want to be a part of the Appinio movement? Here at Appinio, we value transparency in our recruitment process , and we've created a blog to share what to expect during each step of our journey together: here Your mission as an Analytics Engineer We are looking for an Analytics Engineer (m/f/d) with strong data engineering skills to join our Data team and help build a scalable, reliable, and well-structured data foundation across Appinio.

In this role, you will design and maintain clean, scalable data models that make data easy to access, understand, and use across the organisation. You will use SQL, dbt, and Python to transform raw data into meaningful datasets, develop automated workflows, and ensure consistency and reliability across our data warehouse. Beyond data modelling, you will contribute to building, maintaining, and optimising data pipelines and integrations.

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