Staff Data Engineer | Luma

Dotmatics

31d•Remote

About The Position

Dotmatics is seeking a Staff Data Engineer to join their team, focusing on building a powerful AI-Powered R&D platform. Luma is described as an AI-Native Multimodal Research Platform for Scientific Intelligence, designed to streamline scientific research with adaptive R&D workflows driven by AI and seamless data integration. The Staff Data Engineer will be responsible for driving improvements in reliability, performance, and operability, while also contributing to hiring, mentoring, and growing the engineering team. The role requires curiosity and some working knowledge of AI, with a motivation to explore new AI applications in software development and drug discovery. Candidates should be based in the EST time zone and be willing to visit the Boston office infrequently for team meets.

Requirements

12+ years experience in engineering preferably in a SaaS environment
Advanced working experience in Data Engineering pipelines and Asynchronous processing
Expert knowledge of 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.
Advanced working experience with large data processing platforms Spark, Databricks, Snowflake
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
Knowledge of Agile software development practices.
Setting technical direction, leading cross-team initiatives, and leveling up other engineers.

Nice To Haves

Scala
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.

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.