Data Engineer – Oncology Analytics Platform
Data engineer building RayIntelligence, RaySearch's oncology analytics platform that helps radiotherapy clinics turn clinical and operational data into insights. Day to day: designing ingestion/transformation pipelines, analytical data models, and analytics in SQL and Python on a stack of PostgreSQL, SQL Server, dbt, AWS, CDC, and Apache Superset.
- Compensation
- Not disclosed
- Location
- Stockholms län, Sweden
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- Not specified
- Category
- Data engineering
- Posted
Skills
Must have
- SQL development
- Python development
- Data engineering and ELT/ETL development
- Data modeling and data warehousing, including analytical models for complex operational systems
- Applying software engineering best practices to data platforms and production systems
- Translating product and analytical requirements into robust technical solutions
- Working with data quality, observability, testing, or traceability in production data systems
- Several years of professional experience designing, building, and operating production data systems
Nice to have
- Formal education in Computer Science, Engineering, Mathematics, or related field (equivalent practical experience equally valued)
Full description
RaySearch develops software that supports cancer treatment at hospitals and radiotherapy clinics worldwide.
We are looking for a Data Engineer to work on RayIntelligence, an oncology analytics platform helping radiotherapy clinics turn data into actionable insights.
About RayIntelligence
Radiotherapy clinics generate large amounts of clinical and operational data every day. Much of this information remains difficult to access, analyze, and act upon. RayIntelligence is designed specifically for radiotherapy clinics and brings together data from treatment planning systems, oncology information systems, and other clinical and operational sources.
The platform supports everything from treatment workflow analysis, patient volumes, machine utilization, and resource planning to the analysis of treatment patterns and long-term patient outcomes. By making data more accessible and trustworthy, RayIntelligence enables clinics to better understand their operations, learn from past treatments, and continuously improve care.
For healthcare analytics to be useful, clinics need confidence in the data they are using. Metrics must be calculated consistently, results must be understandable, and the path from source data to displayed insight must be possible to verify. The platform is already deployed at customer sites, and we are now focused on expanding both its analytical capabilities and its adoption across clinics worldwide.
Working in healthcare brings unique engineering challenges. Data quality, traceability, reproducibility, validation, and risk management are important parts of how we develop software. While RayIntelligence is not a medical device, we apply many of the same engineering principles and quality practices used throughout RaySearch.
Unlike internal analytics teams, we are building a product for environments where we do not operate the source systems ourselves. Development must therefore account for limited access to real-world customer data, variation between customer environments, and the need to build analytics that clinics can understand and trust.
The platform currently focuses on analytics and reporting use cases, but future AI and machine learning capabilities are expected to build upon the foundations being developed today.
RaySearch supports the use of modern AI-assisted development tools where appropriate, while maintaining the quality standards and engineering rigor required in healthcare software development.
Responsibilities
- Capture and integrate data from clinical and operational source systems
- Design, build, and maintain data ingestion and transformation pipelines
- Develop analytical data models supporting product capabilities and customer use cases