SlipstreamJobs tracks this role from the company's public career site. Apply directly on the employer's site.
Cognite is an industrial digitalization company building AI and data solutions for the world's hardest, highest-impact problems. The company operates at the forefront of industrial AI, offering a comprehensive suite of capabilities including low-code AI agents and data infrastructure to accelerate digital transformation and drive operational improvements.
As a Software Engineer on the Metrics and Marvels team, you will develop Cognite Data Fusion, a robust SaaS platform for industrial data. Your mission is to transform raw data into actionable insights by deciphering complex metrics, uncovering patterns, and providing strategic recommendations that empower customers to make informed decisions and drive growth.
Key responsibilities include:
- Develop and maintain Cognite Data Fusion, a state-of-the-art SaaS platform for industrial data
- Design and implement delightful APIs and robust backend services using Java, Python, and Kotlin
- Build graph database technology leveraging PostgreSQL and Elasticsearch to power Cognite's industrial knowledge graph
- Collaborate with application teams to ensure excellent user experience for solving complex real-world industrial problems
- Build scalable, performant solutions using open-source technologies such as Spark and FoundationDB
- Work with stakeholders and subject matter experts on product and technical decisions
- Contribute to monitoring, troubleshooting, and optimization of production systems
Required experience includes 3+ years of backend software engineering with Java, Python, and/or Kotlin; proficiency with modern database technologies (PostgreSQL, Elasticsearch); strong DevOps practices including CI/CD and Infrastructure as Code; hands-on experience deploying and monitoring software in multi-cloud Kubernetes environments (AWS, GCP, Azure); solid understanding of monitoring tools like Prometheus and Grafana; and excellent communication and collaboration skills.
