Data Engineer

A well-established MedTech and healthcare AI leader transforming real-time clinical diagnostics and emergency care. The company has engineered an advanced, life-saving AI platform that analyzes complex clinical data in real time, detecting critical medical anomalies to empower healthcare providers and acute care teams worldwide. Operating at scale with hundreds of employees globally, the organization combines robust commercial momentum with cutting-edge cloud and big data engineering in a mission-driven culture. The offices are centrally located in Tel Aviv adjacent to the train station, operating on a hybrid schedule with two days working from home. Role Description- Serving as a Lead / Staff Data Engineer, acting as a cross-functional technical pillar across engineering groups to shape data architectures, orchestrate complex initiatives, and establish enterprise data engineering methodologies. Architecting, building, and operating high-throughput, mission-critical Data Infrastructures and Pipelines from scratch to support massive volumes of streaming and batch medical telemetry. Tackling demanding scalability, latency, and reliability challenges across large-scale distributed data frameworks and distributed processing clusters. Designing and implementing modern Data Lakehouse / Data Warehouse architectures optimized for low-latency analytical queries and clinical AI model training. Partnering closely with Data Science, Clinical Research, and Backend teams to translate machine learning pipelines into robust production workflows. Driving best practices in Infrastructure as Code (Terraform), pipeline observability, data governance, and automated CI/CD across cloud environments. Requirements- 8+ years of hands-on experience in Data Engineering – Mandatory Deep expertise in Data Infrastructure design, distributed systems architecture, and building enterprise data platforms from the ground up at massive scale – Mandatory Advanced programming and scripting capabilities in Python – Mandatory High proficiency in advanced SQL development and complex query optimization – Mandatory Proven hands-on experience designing, modeling, and maintaining modern Data Lakehouse or Cloud Data Warehouse solutions – Mandatory Solid background across the AWS cloud ecosystem and distributed streaming/batch tooling (e.g., Spark, Databricks, Kafka, Kinesis, EventBridge, DynamoDB, S3) – Significant Advantage Experience with Infrastructure as Code (Terraform) and containerized cloud workloads – Advantage Show more Show less