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Neko Health is redefining preventative healthcare through data-driven, non-invasive clinical assessments. The company combines proprietary technology with direct clinical care to deliver personalized, actionable health insights before symptoms appear.
As a Data Engineer within the Data-to-AI platform team, you will design and build core data platform components, advanced data models, and ingestion frameworks that power analytics, machine learning, and data-driven decision-making across the organization. You will work with sensitive healthcare data while ensuring strong governance, data quality, and regulatory compliance.
In your first 6–12 months, you will:
• Build and productionize scalable batch and streaming data pipelines supporting platform and research workloads
• Design and implement robust data models across databases and lakehouse systems for analytics and ML
• Establish strong data quality, monitoring, and lineage practices to improve reliability and traceability
• Contribute to scalable, well-governed data platform infrastructure supporting performance, compliance, and growth
• Partner with data scientists, analysts, and engineering teams to enable reliable data contracts and high-quality datasets
Key responsibilities include owning scalable data pipelines for ingestion and processing, architecting data models across databases and lakehouse platforms, developing core data platform components, ensuring data integrity through monitoring and alerting, managing and optimizing data infrastructure, implementing metrics and observability, troubleshooting production issues, and collaborating cross-functionally on governance and integration.
Neko Health operates nearly 100 full-time engineers across Berlin, Chamonix, Hamburg, Lisbon, Marseille, Vilnius, and Stockholm. The company is remote-first, though Stockholm-based software engineers typically work from the office once every one to two weeks. Engineering teams are structured as small, cross-functional groups aligned to specific goals, operating autonomously while collaborating across the organization. Goals are tracked quarterly and annually with bi-weekly progress reviews and a bi-weekly planning cadence.
REQUIREMENTS:
• Strong programming skills in Python and SQL
• Hands-on experience with relational and NoSQL databases, data lakes, and lakehouse technologies (e.g., Delta Lake, Parquet)
• Experience working with cloud infrastructure (Azure, AWS, or GCP)
• Familiarity with CI/CD pipelines and automated testing practices
• Strong data quality and observability experience including metrics, tracing, and alerting
• Experience working with data governance practices including lineage, cataloguing, access control, data contracts, and regulatory compliance
