About the job
Our Mission
Neko Health is pioneering a transformative approach to healthcare, focusing on prevention rather than treatment. We leverage cutting-edge, non-invasive technology along with clinical expertise to provide early, actionable health insights.
Role Overview
As a Data Engineer at Neko Health, you will be instrumental in driving healthcare innovation by developing robust, scalable, and secure data systems within our Data-to-AI platform. You will be responsible for designing and constructing essential components of our data platform, creating advanced data models, and developing ingestion frameworks that facilitate analytics, machine learning, and data-driven decision-making across the organization. With a commitment to handling sensitive healthcare data, you will prioritize governance, data quality, and regulatory compliance in all your initiatives.
Your Contributions in the First 6–12 Months
- Develop and launch scalable batch and streaming data pipelines to support platform and research workloads.
- Design and implement resilient data models across databases and lakehouse systems to enable analytics and machine learning.
- Establish rigorous data quality, monitoring, and lineage practices to enhance data reliability and trustworthiness.
- Contribute to the development of a scalable and well-governed data platform infrastructure that supports performance, compliance, and future growth.
- Collaborate closely with data scientists, analysts, and engineering teams to ensure the integrity and quality of datasets.
Key Responsibilities
- Create and maintain scalable data pipelines for data ingestion, integration, and processing across batch and streaming systems.
- Architect and sustain robust data models within databases and lakehouse platforms that facilitate analytics and machine learning workloads.
- Own the development and maintenance of core data platform components and infrastructure.
- Ensure data integrity and quality through effective monitoring, alerting, lineage, and traceability practices.
- Manage and optimize data infrastructure, including clusters, storage, and compute resources.
- Implement metrics and observability across services using logging, tracing, and monitoring tools.
- Resolve production issues, including pipeline failures and performance bottlenecks.
- Work collaboratively with data scientists, analysts, and backend engineers on modeling, governance, and integration.