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Data Platform Engineer

1 week ago

Ottawa, Ontario, Canada GHGSat Inc. Full time
About GHGSat
GHGSat Inc. offers greenhouse gas detection, measurement, and monitoring services to industrial and government customers worldwide. We utilize our own satellites and aircraft sensors in conjunction with third-party data to provide industrial emitters with a better understanding of their emissions. Our capability is unique: We offer high-resolution, local measurements of atmospheric methane and carbon dioxide concentrations from space, enabling us to detect greenhouse gas emitters and visualize and quantify their emissions.

Data Platform Developer Role
Are you seeking a fast-paced environment where your work can make a meaningful impact? As a Data Platform Developer at GHGSat, you will play a pivotal role in designing, building, and maintaining data and machine learning infrastructure, emphasizing automation, efficiency, and scalability.

You will be part of a small team responsible for designing, building, and deploying infrastructure and tooling for internal stakeholders across the organization. The primary focus of this team is to help internal stakeholders leverage data more effectively to gain new insights, train machine learning models, and develop new products and tools. This role involves providing infrastructure to team members, but in this fast-paced environment, there will be opportunities to engage in traditional data and machine learning engineering work.

Key Responsibilities:
  • Design and implement secure and scalable platform solutions and data pipelines to support internal data science and engineering efforts.
  • Implement tooling and APIs to improve data accessibility and streamline R&D, business intelligence, and product development processes across the organization.
  • Collaborate closely with data scientists, analysts, and other engineering teams to understand requirements and ensure the availability and reliability of services.
  • Develop common practices and architectures to be shared by multiple teams for MLOps and data pipeline tooling.
  • Monitor and manage the health and performance of data infrastructure.
  • Maintain comprehensive documentation of architecture and APIs.
  • Proactively troubleshoot and optimize data products to enhance performance and integrate best practices throughout the data development lifecycle.