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About the Role

We're looking for a Data Engineer to join our Information and AI Platform and own our data pipelines end to end — from data modeling to experimentation to production.

Responsibilities

Own data pipelines end-to-end, from stakeholder metric definition through dbt modeling, QA, and A/B testing, to Dagster scheduling and production.
Transition from a report-request system to a self-service system for coworkers, providing reliable answers for AI agents and a self-describing warehouse.
Maintain steering metrics as governed metrics-as-code, ensuring they are real and trusted.
Manage relevance and content pipelines to ensure the platform reflects real life.
Collaborate with Product, engineering, and other stakeholders to translate data needs into production-ready models.
Ensure data governance in terms of quality, security, and compliance.
Improve personalized recommendations and other ML algorithms with the team.

Requirements

Experience in a data engineering role or similar, with a solid understanding of data warehousing and ETL concepts.
Strong SQL skills and hands-on experience running dbt models in production.
Experience with an orchestration tool (Dagster).
Experience building and running data or information infrastructure that other teams depend on.
Comfortable working directly with backend and infrastructure engineers as well as non-technical stakeholders, and can translate between the two.
Experience with a cloud data warehouse (Snowflake) and a cloud provider (AWS).
Comfortable with version control (Git).
Experience working with AI coding tools like Claude Code, and know how to use them to move faster without cutting corners.
Sound knowledge of GDPR and Article 9 in practice, including designing compliant data access for both people and agents.
Experience taking a ranking or recommendation pipeline through to production.
Familiarity with ML concepts.
Experience or interest in working with data agents and making them reliable.
Fluent in modern data and AI tooling and agentic systems, understanding the importance of curation and semantic context for agent trustworthiness.
Experience leading or mentoring other engineers, or readiness to grow into this role.
Clear communication skills, able to explain complex data concepts to technical and non-technical audiences.
Keeps up with new tools and trends in the field and shares knowledge with the team.
Values diversity, collaboration, and inclusion.
A systems builder, energized by building tools that improve team efficiency and certainty.
Takes ownership of unsolved problems, such as safe AI access to information.

Skills

Data Modeling
dbt
QA
A/B testing
Dagster
SQL
Python
AWS
GitHub Actions
Docker
VS Code
Snowflake
MotherDuck
Git
GDPR
Article 9
ML concepts
Data agents
AI coding tools
Claude Code

Location

Hybrid

Work Type

Hybrid
Full-time

Experience Level

Mid-level
Senior

Benefits

Hybrid work model (at least 2 office days and remote work)
Meaningful job creating a product with real added value
Work in a cheerful and experienced team with idea exchange and mutual learning
Insights into the entire work process of a social startup and the nebenan.de foundation
Responsibility and opportunities to actively contribute
nebenan.de SpenditCard (€35 per month for personal use)
30 days of vacation per year
Possibility of workation models
Sabbatical

About the Company

We are a social startup creating a product with real added value for the community.