(Senior) Data Engineer

As one of our Data Engineers, you help clients get value from their data by building and maintaining reliable, scalable data infrastructure. You design pipelines, ensure data quality, and make data easy to access and use. Your work supports confident, data-informed decisions. You also help improve our internal systems and modernize our tech stack for better performance and scalability.

But your impact doesn’t stop at data loading. You also contribute to the Analytics Engineering workflow - transforming raw data into clean, well-modeled datasets, developing intuitive data models, and enabling self-serve analytics that empower stakeholders to make informed decisions.

As a senior team member, you act as a sparring partner and coach to your colleagues. You’re someone others turn to for advice on technical challenges, project structure, and best practices — and you’re excited to help them grow.

Responsibilities:

• Work across a broad range of technologies in a tooling-agnostic environment, staying current with the evolving data landscape

• Partner with domain experts and client stakeholders to solve complex, high-impact data challenges

• Lead the modernization of our data infrastructure and tech stack to ensure scalability and long-term efficiency

• Define and enforce technical standards for AI and ML projects, ensuring they are production-ready, scalable, and business-relevant

• Mentor team members through code reviews, pair programming, and knowledge sharing

• Drive internal best practices by leading sparring sessions and shaping scalable, maintainable project structures

We believe in a good mixture of experience and upside in our team. We are looking for both types of people equally - for this role, we require more expertise and proof of trajectory.

• 3–4 years of hands-on experience in data engineering or analytics engineering, with a strong focus on building and maintaining robust data pipelines and analytics-ready data models

• Proficient in Python (or a similar scripting language) for use cases such as API integration, data loading, and automation

• Proficient in SQL and experienced with relational databases, capable of translating complex business logic into clear, maintainable queries

• Solid understanding of data modeling techniques (e.g., Kimball dimensional modeling, Data Vault, star/snowflake schema) and data warehousing principles

• Experience working with modern data stack tools, such as Snowflake, BigQuery, Airflow, Airbyte/Fivetran, Git, and CI/CD workflows

• Interest in exploring new technologies and staying up to date (e.g. DuckDB/Motherduck, Apache Iceberg/Delta Lake andDagster)

• Strong communication skills in English (written and spoken), with the ability to explain technical decisions and collaborate with both technical and non-technical stakeholders

• Comfortable working in client-facing projects, navigating ambiguity, and delivering high-quality results with minimal oversight

• Experience coaching or mentoring junior team members through code reviews, sparring, and knowledge sharing

• Bonus: 

  • Docker, Kubernetes

  • ML Engnieering: deployment and monitoring of prediction models

  • Hands-on experience using dbt (preferably dbt Cloud) in production environments, following best practices for modular, testable, and documented code

  • Familiarity with data visualization tools (e.g., Tableau, Power BI, Looker) to support end-to-end workflows or assist analysts

  • Fluency in German

• We have an honest, inclusive work environment and want to nurture this environment
• We have frequent team events in Berlin: our cultural base
• We encourage workations and even do workations as a company - it is up to you to come to the office between one and five days a week
• We don’t compromise on equipment - a powerful Laptop, extra screens, and all the tools you need to be effective
• We will surround you with great people who love to solve (mostly data) riddles
• We believe in efficient working hours rather than long working hours - we focus on the output rather than the input
• We learn and share during meetups, lunch & learn sessions and are open for further initiatives
• We pay a market-friendly salary and we additionally distribute at least 20% of profits to our employees
• We are fast-growing, have technology at our core, yet we do not rely on a VC and operate profitably
• We have a great yearly offsite event that brings us all together for a full week, enjoying good food, having a good time, and of course, solving complex data-related tasks solving complex data-related tasks

1. CV Screening
2. Phone/Coffee/Tea Initial Conversation
3. Hiring Test @home
4. Interviews with 2-3 future colleagues
5. Reference calls
6. Offer + Hired