Backend Engineer

All.Health

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Location: United States
Job Type: full-time
Salary: Unknown
Posted: January 16, 2026

Job Description

All.Health is hiring a Backend Engineer to architect and build scalable systems for ingesting, processing, and managing diverse health data sources—from continuous wearable signals to episodic clinical records. You will work on the backbone of a platform that combines real-time data, AI insights, and personalized care pathways to transform healthcare delivery.

You should be comfortable working with regulated health data (PHI/PII), building privacy-aware and HIPAA-compliant systems, and have strong experience working with event-driven architectures, secure APIs, and health data standards (e.g., FHIR, HL7, BLE protocols). Full-stack experience, especially building internal tools or patient/provider-facing dashboards, is a strong plus.

Core Responsibilities

Design and implement scalable backend services that ingest, transform, and persist diverse health data (wearable sensor streams, EHRs, user inputs).Build APIs and data pipelines for real-time and batch processing, ensuring data integrity, security, and compliance
Integrate with external systems and protocols including FHIR APIs, BLE-based devices, Apple HealthKit, and Google Fit.
Ensure backend services are designed for high availability, reliability, and low latency, especially for real-time clinical decision support
Collaborate with security and compliance teams to ensure HIPAA-compliant handling of PHI/PII data.
Work cross-functionally with product, clinical, and AI teams to align backend capabilities with user-facing experiences and clinical workflows
Contribute to CI/CD pipelines, observability tooling, and data quality monitoring
Support and mentor teammates across engineering functions, especially around secure coding and data access best practices

Requirements

Bachelor’s or Master’s degree in Computer Science or related
5+ years of experience delivering products end-to-end, from ideation through planning and scoping to implementation
Full-stack experience, including building dashboards, admin tools, or data visualizations with React/Next.js, Tailwind, etc
Proven software architecture experience, patterns of large, high-scale applications
Experience integrating 3rd-party health platforms and APIs, including EHRs, remote monitoring tools, or insurance data exchanges
Background in working with data warehousing, analytics platforms, or data lakes for longitudinal health data
Exposure to clinical workflows, remote patient monitoring, or population health management systems
Familiarity with data governance, data provenance, and auditing frameworks for regulated environments
Passion for writing clean, maintainable, and testable code
Excellent programming and computer science fundamentals, and a deep love for technology
Ability to adapt and learn new skills coupled with a resourceful, can-do attitudeOutstanding attention to detailProficient experience with Git, Github, Jira or similar project enablement tools

Technical Requirements

Strong Python backend engineer (FastAPI, Flask, Django) with understanding of distributed systems, microservices, and asynchronous workflows (Celery)
Solid database experience with PostgreSQL or MySQL; comfortable with Kafka or similar event-streaming systems and enforcing data contracts and idempotency
Competent in test architecture using PyTest (fixtures, contract tests), Hypothesis (property-based testing), and Locust (performance/load testing)
Skilled in observability and reliability engineering - instrumenting metrics, traces, and logs to measure and improve real-world system behavior
Hands-on with Kubernetes, containerized CI/CD pipelines, and cloud environments (Azure, AWS, GCP)Familiar with OAuth2, TLS certificate management, and secure service communication
Understands security and compliance principles (HITRUST, HIPAA, FDA 510(k)), encryption, and evidence generation for audits
Experience with HL7 or FHIR interfaces - message parsing, composition, and secure data exchange in healthcare environments
Systems-oriented mindset: treats testing, automation, and monitoring as first-class engineering concerns; pragmatic, collaborative, and effective across the full stackExperience with Java or other JVM-based languages preferred
Experience with Rust

What We're Looking For

Builder mindset with a passion for delivering robust, scalable, and secure systems in healthcare
Comfortable working in a fast-paced, mission-driven environment where data accuracy and integrity are paramount
Willingness to get hands dirty across the stack when needed and collaborate across disciplines
Curious, thoughtful, and focused on long-term maintainability and resilience
Deeply motivated by improving health outcomes through technology

🎯 Who is this job for?

This role is best suited for a senior backend engineer with strong Python experience who enjoys building secure, scalable, and event-driven systems, is comfortable working with regulated healthcare data, and can design reliable APIs and data pipelines for real-time and batch processing. It will appeal to engineers with experience in distributed systems, cloud infrastructure, databases, and observability who are motivated by high data integrity, compliance, and long-term system resilience, and who are excited to collaborate across product, clinical, and AI teams to improve real-world health outcomes.

💬 Potential Interview Questions

What is your experience building backend services in Python with frameworks like FastAPI, Flask, or Django?
I’ve built and maintained microservices in Python primarily with FastAPI and Django REST Framework, designing RESTful APIs, background jobs, and data processing pipelines, with a strong focus on type hints, dependency injection, and clear separation of concerns.
How have you designed event-driven architectures using Kafka or similar streaming systems?
I’ve used Kafka to ingest and process high-volume event streams, modeling topics around domain events, implementing idempotent consumers, managing consumer groups for scalability, and using schemas/contracts to keep producers and consumers aligned.
What is your experience integrating health data standards such as FHIR or HL7?
I’ve integrated with FHIR APIs for patient, observation, and encounter resources, handled OAuth2-secured endpoints, and worked with HL7 v2 message parsing and transformation, ensuring mappings align with clinical workflows and downstream analytics needs.
How do you ensure HIPAA-compliant handling of PHI/PII in backend systems?
I implement least-privilege access, encrypt data in transit (TLS) and at rest, segregate PHI from non-PHI where possible, use strict audit logging on access to sensitive data, and work closely with security/compliance to align with HIPAA, HITRUST, and internal policies.
Describe your experience building real-time and batch data pipelines for health or telemetry data.
I’ve built streaming pipelines for near real-time signals using Kafka + workers (Celery/async consumers) and batch ETL jobs that aggregate, clean, and normalize data into warehouses, ensuring schema evolution is managed and data quality checks run at each stage.
How do you design and optimize relational schemas in PostgreSQL or MySQL for longitudinal health data?
I model entities around patients, encounters, and observations with proper indexing on time and foreign keys, partition large tables when needed, avoid over-normalization that hurts query performance, and profile slow queries with EXPLAIN/ANALYZE to tune them.
What is your approach to observability and reliability in distributed backend systems?
I treat observability as a core feature: instrument structured logs, metrics, and traces; define SLOs and alerts on latency, error rates, and throughput; and use dashboards to monitor trends, feeding incident post-mortems back into reliability improvements.
How have you used Kubernetes and CI/CD pipelines to deploy and operate backend services in the cloud?
I containerize services with Docker, define K8s manifests/Helm charts, and use CI/CD (e.g., GitHub Actions, GitLab, or similar) to run tests, build images, and deploy to staging/production with automated rollbacks, config via environment, and secrets management.
What is your experience with secure API design (OAuth2, TLS, and service-to-service communication)?
I design APIs secured by OAuth2/OIDC, enforce TLS everywhere, use mTLS or token-based auth for inter-service calls, implement rate limiting and input validation, and regularly review endpoints for common vulnerabilities (OWASP) and privacy risks.
How have you collaborated with product, clinical, and AI teams to align backend work with user and clinical needs?
I work from shared specifications and clinical requirements, clarify data semantics and edge cases early, design endpoints and data models that support both AI and clinician workflows, and iterate based on feedback from clinicians, data scientists, and product owners.

📋 Job Summary

All.Health is hiring a Backend Engineer (Remote, US) to design and scale HIPAA-compliant systems that ingest, process, and serve real-time and batch health data from wearables, EHRs, Apple HealthKit, Google Fit, BLE devices, and FHIR/HL7 integrations. You’ll build Python-based microservices (FastAPI/Flask/Django), streaming pipelines with Kafka, and secure APIs on top of PostgreSQL/MySQL, with a strong focus on observability, reliability, and event-driven architectures. The role suits a 5+ year engineer with solid distributed-systems experience, strong testing discipline (PyTest, Hypothesis, Locust), Kubernetes + CI/CD and cloud (AWS/Azure/GCP), and comfort working with OAuth2, TLS, encryption, and auditing in regulated environments (HIPAA/HITRUST/FDA 510(k)). Full-stack experience (React/Next.js, Tailwind) for internal tools or clinical dashboards is a big plus, as is background in clinical workflows, remote patient monitoring, or data lakes for longitudinal health data. Ideal for a systems-minded builder who cares deeply about data integrity, security, and improving health outcomes through AI-powered, real-time care platforms.