Data Engineer II/III

Nimble Robotics•San Francisco, CA

2d•Onsite

About The Position

Help us advance our robotics moonshot by scaling our data engineering efforts. Drive design and development of data infrastructure across our products and internal tools. You will play a critical role in working with our world-class cross-functional team to architect and build the world’s most advanced robotic systems for the world’s biggest robotics opportunity!

Requirements

BS/MS/PhD in Computer Science, Mathematics, Computer Engineering, or a related field, or equivalent practical experience.
Fast learner with a strong work ethic, high performance standards, and ambition.
1-5 years of professional experience
Proficiency in writing production-grade code in Python and SQL.
Hands-on experience with one of the following languages: Rust, Go, Java, C#, C++.
Strong debugging skills and the ability to diagnose and resolve issues efficiently.
Experience with Kafka, Spark and common lakehouse formats like Iceberg, DeltaLake etc.
Experience working with cloud platforms like AWS, GCP, Azure.

Nice To Haves

Experience with Clickhouse, Apache Flink, Databricks
Experience on financial reporting and understanding of accountability

Responsibilities

Design, build, and maintain scalable, reliable data pipelines that support both batch and real-time analytics.
Develop and optimize ETL/ELT processes to ingest, transform, and integrate data from multiple sources into data warehouses and lakehouse platforms.
Drive and apply best practices in data modeling, pipeline architecture, query optimization, data quality and data engineering standards.
Collaborate closely with finance, bizops, and engineering teams to understand data needs and deliver solutions.
Provide data engineering support to ensure data accessibility and usability company-wide.
Implement and maintain data governance frameworks to support compliance with internal policies and external regulatory requirements.
Evaluate and adopt modern data engineering technologies, tools, and best practices to improve scalability, reliability, and operational efficiency.
Document data engineering processes, designs, and architectures