Analytics Engineer, Data Platform

Meyandy LLC; Frankfurt; Vor Ort; EUR 75.000 - 110.000; Vollzeit

Zusammenfassung

Emma – The Sleep Company in Frankfurt is seeking an Analytics Engineer focused on the Data Platform to enable faster, safer, and more reliable data pipelines across the organization.

You will own monitoring, develop with dbt and Paradime, coordinate via MWAA/Airflow, and work across ingestion to BI, partnering with analytics engineers and data scientists.

Qualifikationen

• 3+ years in a data platform or analytics engineering role in production.
• Broad technical knowledge across the data stack and ability to switch contexts.
• Strong SQL and Python; deep understanding of database internals and performance.

Aufgaben

• Own monitoring, alerting, and observability across the data platform.
• Contribute to architecture discussions and set engineering standards.
• Write guides and build internal tooling to improve data workflows.
• Develop and maintain pipelines from ingestion to BI using dbt, Paradime, and orchestration tools.

Kenntnisse

SQL

Python

Analytical thinking

Tools

Redshift

S3

Athena

Glue

dbt

Paradime

Airflow

Airbyte

Fivetran

Terraform

Jobbeschreibung

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.

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).

Who we're looking for:

• 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, picking up unfamiliar problems, with a wide base of technical knowledge across the data stack.
• Strong SQL and Python skills, with a real understanding of how databases work (query execution, performance tuning, storage, access and permission models) and enough dbt experience to review others' work and set standards.
• Working knowledge of the AWS data stack (Redshift, S3, IAM, Athena, Glue) or equivalent, plus experience with lakehouse architectures (Apache Iceberg, Delta, etc), pipeline orchestration (Apache Airflow or equivalent), and modern ingestion/ELT tooling (Airbyte, Fivetran, or equivalent).

Analytics Engineer, Data Platform — Emma – The Sleep Company, Frankfurt.