Senior Software Engineer

ROLE OVERVIEW

The Senior Software Engineer is responsible for designing, implementing, and optimizing features in our next-generation EMR system. You’ll work within a microservices/microfrontend architecture on AWS and contribute to technical decisions, mentoring peers and ensuring quality and scalability of the codebase.

KEY RESPONSIBILITIES

● Design and build robust microservices using Node.js/NestJS and TypeScript; develop scalable React microfrontends.

● Collaborate on service boundaries, domain-driven design, and data ownership strategies across multiple teams.

● Contribute to the design and continuous improvement of CI/CD pipelines and DevOps practices.

● Implement global state management (e.g., Redux or Context API) for shared patient data and other cross-cutting concerns.

● Implement observability, logging, and distributed tracing for production-grade systems. ● Ensure security best practices are embedded throughout the application—from JWT/OAuth-based authentication to encryption and audit logging.

● Develop and maintain RESTful and/or gRPC-based microservices using Node.js/NestJS. ● Architect and implement data models and database strategies across both SQL and NoSQL systems.

● Integrate service registries (e.g., Eureka) and ensure proper containerization and deployment on AWS.

● Work on integrating AI capabilities and modernizing legacy system components incrementally.

● Participate in architecture reviews and contribute to refining coding standards and development best practices.

REQUIRED QUALIFICATIONS

● 5+ years of experience in full-stack development with a strong emphasis on microservices architectures.

● Expertise in Node.js, NestJS, and TypeScript along with front-end development in React. ● Demonstrated experience building and maintaining containerized applications on AWS. ● Familiarity with service registries (e.g., Consul, Cloud Map), CI/CD automation, and observability tools.

● Solid understanding of data modeling and database strategies (both SQL and NoSQL).

● Proven ability to work in agile, cross-functional teams with strong communication skills.

Additional Experience

Databricks & Advanced Data Solutions

1. Spark Development: Leverage Databricks to manage large-scale data processing, batch/streaming jobs, and ML model deployments.
2. Modern Table Formats: Implement and optimize Delta Lake or Iceberg for fast, ACID-compliant transactions and scalable data analytics.
3. Collaboration & Best Practices: Promote best practices for Spark job creation, resource utilization, and distributed data processing to ensure efficient use of Databricks clusters.

Cloud Infrastructure & Integration

1. Multi-Cloud Expertise: Utilize services from both AWS (RDS, Lambda, Glue, S3, Redshift) and Azure (Data Factory, Data Lake Storage, Synapse) to build resilient, cost-effective data solutions.
2. Infrastructure as Code (IaC): Work with DevOps to implement and maintain infrastructure via Terraform, CloudFormation, or ARM/Bicep templates where applicable.
3. Cross-Functional Collaboration: Partner with DevOps to ensure robust monitoring, logging, and alerting solutions are in place (e.g., Datadog, ELK Stack, CloudWatch, Azure Monitor) for all data pipelines.

Data Governance, Security & Compliance

1. Data Governance & Quality: Develop and enforce data governance policies, standards, and frameworks—ensuring high data quality, lineage, and stewardship.
2. Healthcare Compliance: Ensure compliance with healthcare data regulations (e.g., HIPAA), implementing robust access controls, audit trails, and encryption strategies.
3. Security Monitoring: Implement advanced security measures and monitor data access patterns, collaborating with the InfoSec team to conduct scans, penetration tests, and incident response drills.

Performance Optimization & Troubleshooting

1. Data Performance: Continuously evaluate and optimize data storage/queries for performance and cost efficiency at scale (SQL tuning, partitioning strategies, caching).
2. Monitoring & Alerting: Set up proactive alerting and monitoring systems (e.g., Datadog, Prometheus, Grafana, CloudWatch Metrics) to promptly identify and address pipeline bottlenecks and failures.
3. Incident Management: Investigate and resolve data-related issues, working closely with DevOps, Data Science, and Software Engineering teams to minimize downtime and maintain SLAs.