Data Platform Engineer

ESSENTIAL DUTIES AND RESPONSIBILITIES

• Design and maintain backend data services and pipelines for ingesting, processing, and serving telemetry, sensor, and training data generated across development and deployed fleets.
• Build robust batch and streaming data workflows that integrate on-robot data sources, cloud infrastructure, and enterprise systems.
• Develop internal APIs and platform tooling that enable machine learning, robotics, and software teams to access trusted data efficiently and securely.
• Establish data quality, lineage, and governance practices that improve confidence in datasets used for model training, analytics, and operational decision-making.
• Maintain and optimize storage systems, database performance, and resource utilization to meet scalability, throughput, and latency requirements.
• Collaborate closely with data scientists, machine learning engineers, robotics engineers, SRE, and security teams to deliver production-ready data infrastructure.
• Support secure deployment patterns including encryption, access controls, and reliable operation across cloud and hybrid environments.
• Stay current on data engineering practices, distributed systems design, and emerging technologies relevant to robotics and machine learning platforms.

Skills And Requirements

• Programming: Strong proficiency in Python; experience with Go for backend service implementation is preferred.
• Data Engineering: Experience with real-time and batch pipeline frameworks such as Kafka, Spark, Airflow, or comparable technologies.
• Databases: Strong command of relational databases such as PostgreSQL and familiarity with NoSQL stores; experience with time-series data is a plus.
• Cloud and Infrastructure: Proficiency with cloud platforms and hands-on experience with infrastructure tooling such as Terraform, Helm, or Ansible.
• Containerization: Experience with Kubernetes and Docker for deploying and scaling backend and data services.
• Security: Familiarity with encryption, RBAC, and secure service-to-service or user-to-service data access patterns.
• Monitoring: Experience building observability dashboards and alerting for data pipelines and platform services.
• API Design: Experience building REST APIs or gRPC services for data access, integration, and internal platform use.

Preferred Qualifications

• Experience in robotics, autonomous systems, data platforms, or machine learning infrastructure.
• Familiarity with time-series data stores such as InfluxDB or TimescaleDB for telemetry-heavy workloads.
• Experience with data catalog, lineage, or governance tooling.
• Knowledge of streaming architectures and event-driven systems in production environments.

Education And/or Experience

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field.
• Minimum of 3 years of professional, full-time experience in data engineering, backend engineering, or a closely related discipline.
• Experience building data pipelines or platform infrastructure used to support machine learning, analytics, or AI workflows.