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Dotmatics

Cloud-based R&D informatics for life sciences

Staff Data Engineer - Luma

Backend EngineeringPosted on Nov 13
Full-timeMexico City, CDMX, MexicoMX$900.9k - MX$1.8MIn-person

About the Job

Requirements

  • Candidates must have 12 or more years of engineering experience, preferably in a Software as a Service environment.
  • Candidates must have excellent written and spoken English communication skills and collaborate effectively across globally distributed engineering and product teams.
  • Candidates must have a language-agnostic engineering mindset, quickly learn new languages and codebases, reason about distributed system design independently of a single runtime, and become productive in an unfamiliar stack within weeks.
  • Candidates must have advanced working experience in asynchronous processing.
  • Candidates must have strong proficiency in Node.js, TypeScript, and Python for building production backend services.
  • Candidates must have proven experience designing and implementing distributed, event-driven systems coordinated through message buses, GraphQL, and RESTful APIs.
  • Candidates must have experience implementing automated testing platforms, unit tests, and integration tests.
  • Candidates must have advanced working experience with large data processing platforms such as Spark, Databricks, or Snowflake.
  • Candidates must have experience managing state across multiple stores, such as a message bus, relational database, and cache, and handling the associated consistency challenges.
  • Candidates must have hands-on experience with AWS in production environments and a solid understanding of Kubernetes for orchestrating workloads.
  • Candidates must be proficient with continuous integration and continuous delivery tools such as GitHub Actions.
  • Candidates must know Agile software development practices.

Responsibilities

  • Define and drive system architecture for event-driven data processing services, leading migration of key components to a Node.js, TypeScript, and Python ecosystem.
  • Contribute to and improve engineering standards, patterns, and best practices for distributed systems, observability, and reliability across a multi-service platform coordinated through Kafka, GraphQL, and RESTful APIs.
  • Architect and implement asynchronous data processing pipelines for high-volume scientific data, with attention to at-least-once delivery guarantees, backpressure, and graceful degradation.
  • Ensure the scalability, maintainability, and security of software solutions running on Kubernetes and AWS.
  • Take ownership of existing services, develop a deep understanding of their behavior, and make pragmatic decisions about how to evolve them.
  • Set technical direction, lead cross-team initiatives, and develop other engineers through mentoring and architectural guidance.

Desired Qualifications

  • A degree in Computer Science, Software Engineering, or an equivalent field is preferred.
  • Experience with Scala or other JVM languages is helpful for understanding existing services during migration.
  • Experience with message-based architectures using Kafka, including consumer groups, partitioning, and delivery semantics.
  • A background in complex data pipelines, schema management, and incremental processing.
  • Exposure to AWS or Google Cloud Platform, or experience designing systems portable across multiple cloud providers.
  • Experience building scalable distributed systems using Kubernetes and other cloud-native technologies.
  • Experience within Life Sciences or research and development data management.

About the Company

Dotmatics

Cloud-based R&D informatics for life sciences

Dotmatics provides a cloud-based R&D software platform for life sciences, chemistry, biology, and materials research that helps researchers capture, analyze, visualize, and share experimental data. Its suite combines data management, workflow automation, and analytics, with tools like Luma for low-code multimodal drug discovery and applications such as GraphPad Prism, SnapGene, and Geneious. The platform harmonizes diverse scientific data in a single environment to improve collaboration, reduce manual work, and speed up discovery, used by millions of scientists across thousands of organizations. In 2025, Dotmatics was acquired by Siemens and integrated into Siemens Digital Industries Software, expanding Siemens' AI-driven product lifecycle capabilities into life sciences.

Company size
501-1,000
Company stage
Acquired
Total funding
$106M
Headquarters
Boston, MA
Founded
2005

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