Python Engineer

An established, high-impact HealthTech enterprise developing an AI-powered clinical data platform designed to assist medical teams in identifying time-sensitive, life-critical conditions in real time.

Combining deep learning analytics with cloud-native workflows, the company serves major healthcare systems and hospitals globally. With approximately 400 employees, they offer long-term stability and high-scale engineering challenges directly connected to life-saving technologies.

Located in a primary high-tech hub in the Tel Aviv area with immediate train and public transit proximity, operating on a flexible hybrid work model.

Position Overview-

• Senior Data Infrastructure Engineer joining a core Data Infra squad responsible for architecting and scaling the company's enterprise-wide data foundation.
• Building and optimizing robust, high-throughput batch and streaming data pipelines to handle complex clinical telemetry and real-time alerts.
• Designing event-driven data workflows, streaming architectures, and infrastructure automation setups across AWS environments.
• Implementing data transformation frameworks, monitoring observability metrics, and ensuring strict compliance, reliability, and low latency for mission-critical medical data.
• Partnering closely with Data Science, ML Engineering, and Core R&D teams to streamline data accessibility and model deployment.
• Core Domain & Ecosystem- Data Infrastructure, High-Throughput Data Pipelines, Python, Cloud Provisioning (AWS), Event-Driven Architecture (EventBridge, SQS, SNS, DynamoDB, Kinesis, S3), Distributed Processing (Spark, Kafka, Airflow, dbt), IaC (Terraform).

Requirements-

• 6+ years of robust professional experience in software engineering with a primary focus on data-intensive systems – Mandatory
• Deep, hands-on technical expertise in Data Engineering & Data Infrastructure (designing pipelines, streaming architectures, and data warehouses) – Mandatory
• Advanced, production-grade programming mastery in Python – Mandatory
• Extensive hands-on experience building, deploying, and managing data ecosystems inside AWS – Mandatory
• Proven experience with event-driven architectures and messaging queues (e.g., Kafka, Kinesis, SQS, SNS, EventBridge) – Mandatory
• Practical familiarity with modern data orchestration and processing tools (Spark, Airflow, dbt, DynamoDB) – Strong Advantage
• Experience with Infrastructure as Code (Terraform) in cloud-native environments – Strong Advantage
• Exceptional problem-solving capabilities, strong architectural vision, and a passion for building mission-critical, life-saving infrastructure – Mandatory