Java Software Engineer

An established, high-growth Medical Tech enterprise that has engineered an AI-driven clinical intelligence platform capable of identifying critical, time-sensitive medical anomalies in real time to assist medical teams in life-saving decisions.

Deployed across major global healthcare institutions, the company’s platform processes massive streams of complex clinical data under strict uptime and regulatory standards.

The organization maintains a robust technological footprint, housing over 400 professionals globally.

The company's primary R&D center is located in Tel Aviv within short walking distance of the train station, operating under a flexible hybrid structure that supports two days of remote work per week.

Position Overview-

• Senior Data Infrastructure Engineer joining the core Data Infra cell responsible for architecting, scaling, and maintaining the company’s unified, high-throughput data processing ecosystem.

• Designing and building resilient, fault-tolerant data pipelines capable of handling high-velocity medical telemetry, multi-modal clinical images, and real-time streaming updates.

• Owning the foundational cloud infrastructure and automated infrastructure-as-code (IaC) deployment definitions to support highly available data platforms.

• Collaborating cross-functionally with Data Science, Backend Engineering, and Platform teams to deliver scalable data storage, extraction, and transformation capabilities.

• Core Stack & Ecosystem- Python, JVM Ecosystem (Java / Scala / Kotlin), AWS, Terraform, Distributed Streaming (Kafka, Kinesis), Big Data Processing (Spark), Orchestration & Transformation (Airflow, DBT), Messaging (EventBridge, SQS, SNS), and NoSQL/Object Storage (DynamoDB, S3).

Requirements-

• 7 years of proven professional experience operating as a Data Engineer or Backend Software Engineer building enterprise-grade architectures – Mandatory

• Production-proven development mastery within the JVM ecosystem (Java, Scala, or Kotlin) – Mandatory

• Extensive, hands-on experience designing and operating distributed systems within Cloud environments (AWS preferred) – Mandatory

• Practical experience engineering large-scale, production-ready Data Pipelines or distributed stream processing engines (e.g., Spark, Kafka, Airflow) – Mandatory

• Deep technical familiarity with automation, infrastructure-as-code (Terraform), and building highly resilient data layers – Significant Advantage