Our client is a remote-first SaaS product company based in Berlin, building data-intensive solutions that power advanced analytics and operational decision-making.
They work with complex, high-volume data flows and rely on a modern, well-structured engineering stack to keep things reliable, transparent, and scalable.
What makes this environment attractive for engineers:
You work end-to-end with real data products, not abstract pipelines.
You collaborate with a small, experienced team that values clean code, simplicity, and engineering discipline.
You use modern tooling — Python, Prefect, SQL, Metabase, cloud services — without legacy overload or bureaucratic overhead.
You get clear requirements, manageable scope, and the ability to make meaningful improvements.
You grow by solving practical, non-trivial data challenges that directly impact the product.
This is a mid-level role designed for engineers who already have solid Python + ETL experience and want to deepen their expertise with workflow orchestration, data quality, and modern BI tooling.
You will build reliable pipelines, improve data accuracy, and support the team in turning raw data into clean, usable datasets and dashboards.
You won’t be expected to lead projects or mentor others — but you must be able to independently handle well-defined engineering tasks.
You will work alongside senior engineers and product stakeholders to maintain and evolve the data platform. Your work will include:
Develop and maintain ETL pipelines using Python and Prefect
Write clean, tested code for transformations, ingestion jobs, and integrations
Optimize pipeline performance and reliability
Implement validation rules, sanity checks, and lightweight monitoring
Investigate and resolve data issues in collaboration with the team
Contribute to improving data consistency and transparency
Help develop new data models and contribute to schema design
Build and maintain dashboards in Metabase to support internal stakeholders
Support analytical reporting by preparing clean datasets
Translate business requirements into clear engineering tasks
Document your work and follow internal best practices
Communicate effectively with engineering, product, and business teams
3–5+ years in Data Engineering or Python Engineering
Strong proficiency in Python (pandas, SQLAlchemy, typing, testing)
Hands-on experience with ETL orchestration (Prefect preferred; Airflow acceptable)
Solid SQL skills and familiarity with relational databases
Experience with data quality checks, validation, or monitoring
Experience with Metabase or similar BI tools
Understanding of cloud-based data workflows (AWS, GCP, or Azure)
Ability to work independently on assigned tasks
English proficiency and clear communication
dbt or similar transformation tools
Experience with NoSQL databases
Basic analytics or statistics knowledge
Exposure to APIs or light backend work
Experience in distributed remote teams
Work in a small, senior, engineering-driven environment
Fully remote culture with flexible hours
Modern stack (Python, Prefect, SQL, Metabase, AWS/GCP/Azure)
Real opportunity to deepen ETL & data quality expertise
Competitive compensation depending on location
Personal development support
Optional office access on the tech campus near Berlin
Regular virtual and onsite team meetups