Data Platform Engineer

JOB SUMMARY

Apptronik is seeking a Data Platform Engineer to join our Data Platform team. In this role, you will build and maintain the backend systems that ingest, organize, and serve the robotic telemetry, sensor, and training data used across model development, fleet analytics, and operational reporting. You will work at the intersection of robotics, cloud infrastructure, and machine learning, ensuring data is reliable, well-governed, and accessible across both cloud and hybrid deployment environments. Your work will provide critical infrastructure for improving Apollo in development and in production.

ESSENTIAL DUTIES AND RESPONSIBILITIES or KEY ACCOUNTABILITIES

• 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.
• Monitor 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.

PHYSICAL REQUIREMENTS

• Prolonged periods of sitting at a desk and working on a computer
• Must be able to lift 15 pounds at times
• Vision to read printed materials and a computer screen
• Hearing and speech to communicate