Key skills
GolangPythonKubernetesETL processesCloud platformsData modelingCI/CD pipelinesMonitoring toolsOrchestration toolsProblem-solving skillsAIMachine LearningETLAnalyticsAirflowApache AirflowAWSAzureGCPDockerHelmPrometheusGrafanaELK StackCI/CD
About this role
Machinify is a leading healthcare intelligence company focused on delivering value and efficiency to health plan clients. They are seeking a skilled Senior Software Engineer to join their Data Platform Development Team, which builds scalable and reliable infrastructure for data processing and analytics across products and customer-facing systems.
Responsibilities:
- Design and build scalable and high-performance data software solutions using Golang and Python
- Build and deploy Kubernetes-based systems to manage containerized applications in cloud-native environments
- Collaborate with cross-functional teams to understand and address customer needs, ensuring our systems evolve to meet future requirements
- Optimize performance, reliability, and security of our backend infrastructure
- Work on data modeling and transformation to support machine learning workflows and AI-powered applications
Requirements:
- 6+ years of hands-on experience in backend software development, with expertise in Golang and/or Python
- Strong understanding of ETL processes, data pipelines, and working with large datasets
- Experience with Kubernetes orchestration, containerization, and cloud-native development
- Proficiency in building scalable systems that handle high volumes of data and transactions
- Knowledge of cloud platforms (AWS, Azure, or GCP) and their ecosystem tools
- Familiarity with monitoring and observability tools (e.g., Prometheus, Grafana)
- Strong problem-solving skills and a passion for writing clean, maintainable code
- Experience with data modeling, transformation, and integration
- Golang and Python experience
- Experience with orchestration tools (e.g., Apache Airflow, Prefect)
- Familiarity with Kubernetes and container orchestration (Docker, Helm, etc.)
- Cloud platform experience (AWS, Azure, or GCP) and related services
- Knowledge of monitoring and logging tools for distributed systems (Prometheus, Grafana, ELK stack)
- Experience with CI/CD pipelines and automated testing
- Strong understanding of data modeling, ETL workflows, and data transformation techniques