Key skills
TypeScriptRAIKafkaReactNode.jsAWSGCPTerraformKubernetesGitHub ActionsServerlessCodePipelinePostgreSQLRESTfulGitHubSaaSAgileCI/CDMentoring
About this role
Dotmatics is a company dedicated to intertwining science, data, and decision-making for innovation in the scientific research field. They are seeking a Staff Engineer to enhance their AI-Powered R&D platform, Luma, focusing on system architecture, engineering standards, and mentoring a skilled engineering team.
Responsibilities:
- Define and drive system architecture for services within a Node.js/TypeScript ecosystem
- Contribute to and improve engineering standards, patterns, and best practices for distributed systems, observability, and reliability
- Architect and implement serverless and event-driven data processing pipelines for high-volume scientific data
- Guarantee the scalability, maintainability, and security of software solutions
- Provide technical guidance to Software Engineers, conduct code reviews, and raise the bar for design, code quality, and operational excellence
- Help shape user interfaces that are intuitive and accelerate scientific research
- Collaborate with other teams to build scientific solutions on top of a best-in-class scientific data engine
Requirements:
- 12+ years experience in engineering preferably in a SaaS environment
- Degree in Computer Science, Software Engineering, or equivalent
- Strong engineering background with Node.js and React
- Proven experience designing and implementing distributed, event-driven systems and high-level web applications
- Experience implementing automated testing platforms, unit tests, and integration tests
- Professional experience with PostgreSQL and building/consuming RESTful APIs
- Hands-on experience with AWS in production environments and a solid understanding of Kubernetes for orchestrating workloads
- Proficiency with CI/CD tools such as GitHub Actions and AWS CodePipeline
- Knowledge of Agile software development practices
- Setting technical direction, leading cross-team initiatives, and levelling up other engineers
- Background in complex data pipelines and scaling
- Message-based architectures (e.g., Kafka)
- Deployment technologies like Terraform
- Exposure to AWS and/or GCP or designing systems portable across multiple cloud providers
- Building Windows applications
- Building scalable distributed systems using Kubernetes and other cloud-native technologies
- Experience within Life Sciences or R&D data management