Senior Software Data Platform Engineer

Our Data Platform powers the Location Engine and predictive analytics pipelines — processing high-volume real-time IoT telemetry and transforming it into production-grade location intelligence for hospitals, clinics, and healthcare networks worldwide.

• 6+ years of software engineering experience with distributed systems at scale.
• Proven experience leading architecture proposals and driving them to production.
• Deep Kafka expertise — Kafka Streams, event-driven architecture design, high-throughput pipeline operation.
• Strong Kubernetes skills — deploying, operating, and optimizing containerized workloads in production.
• Experience with Spring Boot or a comparable JVM microservices framework.
• Hands-on AWS experience including infrastructure as code (Terraform, CDK, or equivalent).
• Solid CI/CD, test automation, and version control best practices.
• Demonstrated use of AI coding tools in daily workflows (Claude, GitHub Copilot, or equivalent).
• Strong written and verbal communication in English.

Nice to have:

• Working knowledge of HIPAA compliance requirements in software systems.
• AWS EMR experience (Spark, Flink, or large-scale batch compute).
• Experience integrating with EHR systems (Epic, Cerner, HL7/FHIR).
• Background in healthcare IoT or real-time location systems.

Platform architecture:

• Lead proposals and hands-on implementation of scalable, AI-augmented data platform architecture — from streaming pipelines to predictive analytics at scale.

Privacy & compliance:

• Design and maintain secure systems with encryption at rest and in transit, audit logging, access controls, and proactive observability — built for regulated healthcare environments.

Performance engineering:

• Identify and eliminate bottlenecks across streaming pipelines, microservices, and data stores.
• Own performance and load testing for compute-intensive paths.

Infrastructure as code:

• Build and maintain cloud-native AWS infrastructure using IaC tooling.

• Design containerized, Kubernetes-native workloads with a focus on cost, reliability, and zero-downtime deployments.

• Architect and implement streaming & batch data pipelines handling high-volume real-time location telemetry and predictive analytics workloads.

• Apply and champion AI-first engineering practices — from Claude and Copilot-assisted development and code review to LLM-augmented observability and automated testing workflows.

• Define and evolve data models across PostgreSQL and other relational or columnar stores.

• Build and maintain event-driven systems using Kafka and Kafka Streams.

• Ensure low-latency, high-availability workloads running in Kubernetes.

• Integrate with third-party and custom in-house hospital systems — including proprietary location prediction engines, patient flow platforms, and clinical data feeds — via REST APIs, Webhooks, and event-based architectures.

• Contribute to architectural decisions impacting scalability, resilience, and platform evolution.

• Write performance and load tests for compute-heavy services.

• Maintain and improve CI/CD workflows for reliable production deployments.

• Participate in 24/7 on-call rotation for platform reliability.

• Can independently design and implement scalable data processing systems.

• Understands problems from a product and system architecture perspective.

• Proposes pragmatic, high-impact solutions and executes them end-to-end.

• Thinks in terms of performance, reliability, observability, and cost-efficiency.