Senior Data Engineer

June 4

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Intuition Machines

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machine learning and AI

51 - 200

Description

• Maintain, extend, and improve existing data/ML workflows, and implement new ones to handle high-velocity data • Provide interfaces and systems that enable ML engineers and researchers to build datasets on demand • Influence data storage and processing strategies • Collaborate with the ML team, as well as frontend and backend teams, to build out our data platform • Reduce time-to-deployment for dashboards and ML models • Establish best practices and develop pipelines that enable ML engineers and researchers to efficiently build and use datasets • Work with large datasets under performance constraints comparable to those at the largest companies • Iterate quickly, with a focus on shipping early and often, ensuring that new products or features can be deployed to millions of users

Requirements

• Thoughtful, conscientious, and self-directed • Experience working with data engineering services on major cloud providers • Minimum of 3 years of experience in a data role involving designing and building data stores, feature engineering, and building reliable data pipelines that handle high loads • Proven ability to make independent decisions regarding data processing strategy and architecture • At least 2 years of professional software development experience in a role other than data engineering • Significant experience coding and developing in Python • Experience in building and maintaining distributed data pipelines • Experience working with Kafka infrastructure and applications • Deep understanding of SQL and NoSQL databases (preferably Clickhouse) • Familiarity with public cloud providers (AWS or Azure) • Experience with CI/CD and orchestration platforms: Kubernetes, containerization, and microservice design. Familiarity with distributed systems and architectures

Benefits

• Fully remote position with flexible working hours #Li-remote • An inspiring team of colleagues spread all over the world • Pleasant, modern development and deployment workflows: ship early, ship often • High impact: lots of users, happy customers, high growth, and cutting-edge R&D • Flat organization, direct interaction with customer teams