Analytics Engineer, Data Platform
The Analytics Engineer will design, build, and maintain data pipelines and observability tooling on Emma’s AWS‑based lakehouse, using SQL, Python, dbt, Airflow, and IaC, while documenting standards and supporting AI‑augmented workflows.
- Compensation
- Not disclosed
- Location
- Germany, Portugal
- Work setup
- Remote
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 3+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 3+ years in a data platform, data engineering, analytics engineering, DataOps, or closely related role in a production environment.
- Breadth over depth in a single tool – comfortable switching between topics and picking up unfamiliar problems with a wide base of technical knowledge across the data stack.
- Strong SQL and Python skills with understanding of database internals and enough dbt experience to review work and set standards.
- Working knowledge of the AWS data stack (Redshift, S3, IAM, Athena, Glue) or equivalent, plus experience with lakehouse architectures, pipeline orchestration (Airflow or equivalent), and modern ingest
- Exposure to Infrastructure as Code (Pulumi, Terraform, or equivalent) and confidence navigating internal tooling and existing codebases.
- Strong communication and writing skills; able to move between stakeholder conversations and technical discussions.
- Comfortable writing documentation, guides, and architecture decisions.
- Initiative and autonomy to drive work forward independently after onboarding, raising blockers early.
- Fluent English, written and spoken; based in Europe with workable timezone overlap.
Full description
Ready to lead, disrupt and reinvent the sleep industry?
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.
What you will do:
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.