Analytics Engineer

Emma Sleep; Frankfurt; On-site; EUR 90,000 - 130,000; Full time

Job summary

Emma Sleep is seeking a data platform professional to join a cross-functional Data Platform Team serving analytics engineers, data scientists, and analysts. You will own and improve the data platform’s reliability, governance, and tooling across ingestion to BI consumption, with a strong emphasis on observability and standards.

Expect to work across the full stack, contribute to architecture decisions, and drive AI adoption within our data infrastructure.

Qualifications

• 3+ years of experience in a data platform, data engineering, Analytics engineering, or DataOps
• Proficient with AWS data stack (Redshift, S3, IAM, Athena, Glue) or equivalents
• Familiar with lakehouse architectures (Iceberg, Delta) and orchestration (Airflow)
• Experience with modern ingestion/ELT tooling (Airbyte, Fivetran, etc.)
• Ability to write documentation, guides, and architecture decisions
• Fluent in English, written and spoken, Europe-based with timezone overlap
• Strong communication and writing; can move from stakeholder talks to technical solutions
• Strong SQL and Python skills with understanding of DB internals and performance
• Experience with dbt and reviewing others’ work to set standards
• Initiative and autonomy; comfortable with IaC (Pulumi, Terraform) and internal tooling

Responsibilities

• Own and improve monitoring, alerting, and observability across the data platform
• Contribute to architecture discussions with ADRs and RFCs
• Set and enforce engineering standards across lakehouse, orchestration, and data warehouse
• Build internal tooling to remove friction for data teams
• Support AI adoption within the data infrastructure
• Hands-on pipeline and model development using AWS data stack
• Enhance Redshift data warehouse with dbt and Paradime
• Orchestrate data pipelines and manage IaC-based provisioning
• Write clear guides and documentation to enable cross-team alignment
• Ingest data via ELT, containerised Python, and event-based patterns

Skills

Data platform

Analytics engineering

SQL

Python

Communication

Stakeholder mgmt

Documentation

Architectural decisions

Problem solving

English proficiency

Tools

dbt

Paradime

Airflow

Pulumi

Terraform

Redshift

S3

Athena

Iceberg

Delta

Job description

• 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)

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 stack3+ years in a data platform, data engineering, analytics engineering, DataOpsWorking 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)Comfortable writing documentation, guides, and architecture decisionsFluent English, written and spoken; based in Europe with workable timezone overlapStrong communication and writing. You can move between a stakeholder conversation about a problem and a technical discussion about how to solve itStrong 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 standardsInitiative and autonomy to drive work forward independently after onboarding, raising blockers earlyExposure to Infrastructure as Code (Pulumi, Terraform, or equivalent), and confidence navigating internal tooling and existing codebases