Analytics Engineer, Data Platform

We are Emma – The Sleep Company. Founded in 2015, we have grown into the world’s largest direct-to-consumer (D2C) sleep brand, with a presence in over 20 markets and more than 35 Emma stores across Europe. 

Our mission is simple: to develop sleep comfort products that empower our customers to awaken their best every day. Today, our products are trusted by millions and recommended by leading consumer associations worldwide. 

It’s our people who bring this mission to life. At Emma, you’ll join a driven, international team that values ownership, collaboration, and continuous knowledge sharing. With colleagues from over 70 nationalities, we combine diverse perspectives with a shared ambition to learn, grow, and create lasting impact, together. 

Ready to awaken your best with us? 

You'll join a cross-functional Data Platform Team serving Emma's broader Data and Tech organisation, with internal users spanning analysts, analytics engineers, data scientists and data engineers. The team's mission is to make that work faster, safer, and more reliable - through observability, access control, engineering standards, code reviews, internal tooling, and AI adoption across the full pipeline from ingestion to BI tool consumption.

While the primary focus is analytics engineering tooling and process, you'll operate across the full stack, working closely with the Staff Analytics and Data Engineers to scope, build, and ship, while raising the bar for how the team builds. The role rewards breadth and initiative over deep specialisation in one layer, requiring frequent context-switching and comfort with unfamiliar problems.

Reliability, Standards & Governance

Own and improve monitoring, alerting, and observability across the data platform, so failures are caught early and pipeline/model health is visible to teams.

Contribute to architecture discussions: propose improvements, document trade-offs (ADRs, RFCs), and help decide what to build, refactor, or retire.

Set, document, and enforce engineering standards and best practices across our lakehouse, orchestration layer, data warehouse, and reporting systems, including code review culture.

Enablement & Internal Tooling

Write clear guides, standards, and documentation that help colleagues across the Data domain work more efficiently and consistently, driving alignment through knowledge-sharing forums.

Build and maintain internal tooling that removes friction for the teams you serve (e.g. AI-augmented workflows, extending observability and quality frameworks).

Support AI adoption within our data infrastructure, in collaboration with the broader tech division.

Hands-On Pipeline & model development 

Enhance and build on our Redshift data warehouse using dbt and Paradime.

Orchestrate execution and dependencies between up/downstream pipelines (MWAA, Paradime), provisioning infrastructure via IaC (Pulumi, Terraform) so changes stay reproducible and version-controlled.

Contribute to our ingestion pipelines across three core patterns - simple ELT, containerised Python, and event-based - landing data reliably into our medallion lakehouse (S3, Glue, Iceberg).