Data Engineer

Your mission

As a Senior Data Engineer, you will play a key role in building the data backbone of mama health’s real-world evidence platform. We use data mining and analytics to systematically map the end-to-end experiences of chronic patients—because today, researchers, pharmaceutical companies, and governments often can’t reliably observe what happens to patients along their real care path. They rely on theoretical protocols (what should happen), but lack tools to monitor and understand the reality (what actually happens).

We bridge this gap by transforming patient-generated data into structured, high-quality evidence that can improve decision-making and, ultimately, patient outcomes.

In this role, you will work hands-on with data from multiple sources and formats, including event data, operational systems, partner datasets, and unstructured inputs such as free text. You will help turn these raw inputs into reliable, analytics-ready datasets that power patient journey analysis, product features, and reporting for pharmaceutical partners. This includes building and maintaining pipelines, defining consistent data models, and enabling fast and trustworthy access to data for analytics, product, and AI teams.

You’ll also contribute to the operational excellence of our data ecosystem: implementing monitoring and quality checks, improving pipeline reliability and performance, and helping us evolve the platform as our product and data volume grow. Working closely with cross-functional stakeholders (product, AI, data science, and healthcare experts), you will translate real-world needs into pragmatic data solutions—balancing speed and iteration with a high bar for privacy, security, and correctness in a sensitive healthcare context.

Your work will directly support our mission to transform healthcare by leveraging the collective wisdom of patients and generating real-world insights that accelerate research and improve lives.

• Bachelor’s or Master’s degree in Computer Science, Data Engineering, Data Science, or a related field.
• 3+ years (or equivalent) experience in a Data Engineering / Analytics Engineering / Backend-leaning data role.
• Strong programming skills in Python and solid SQL skills (joins, window functions, performance basics).
• Solid understanding of ETL/ELT concepts and experience building or maintaining data pipelines (batch and/or streaming).
• Experience working with relational databases (e.g. Postgre
  
  SQL/MySQL) and familiarity with analytical storage (e.g. Big Query/Snowflake/Redshift) is a plus.
• Familiarity with orchestration or transformation tooling (e.g. Airflow, dbt, Dagster, Prefect).
• Ability to clean, transform, and validate large datasets, and to implement practical data quality checks.
• Interest or experience in extracting structured signals from unstructured data (e.g. free text / conversations) to support analytics.
• Basic experience with monitoring/observability for pipelines (logs, alerts, SLAs) and willingness to own reliability.
• Familiarity with cloud platforms (preferably AWS; GCP/Azure also fine) and containerization (e.g. Docker).
• Knowledge Graph is a plus but highly valued.
• Good engineering hygiene:
  Git, code reviews, small frequent commits, tests, CI/CD (to the level appropriate for data systems).
• Strong communication skills and willingness to collaborate with product, analytics, and AI teams.
• Comfortable working in a fast-paced startup environment with iterative delivery.
• This role requires you to be based in Berlin.