Data Engineer II

Nimble•San Francisco, CA

39d•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

Fast learner with a strong work ethic, high performance standards, and ambition.
1-4 years of experience in a tech company or fast-growing startup.
BS/MS/PhD in Computer Science, Mathematics, Computer Engineering, or a related field, or equivalent practical experience.
Proficiency in writing production-grade code in at least one of: Python, Java, Rust, Go, or Scala.
Strong debugging skills and the ability to diagnose and resolve issues efficiently.
Experience with Kafka and/or Spark.
Experience working with AWS or other cloud platforms.

Nice To Haves

Experience with modern data engineering tools like Databricks, Clickhouse, DeltaLake, Apache Iceberg, Apache Flink, Parquet, Apache, Arrow Airflow
Experience with Robotics, Machine Learning and Mechanical Engineering

Responsibilities

Design and maintain scalable, reliable data pipelines.
Ensure timely and efficient data collection, processing, and availability.
Integrate data from multiple sources for both batch and streaming analytics.
Manage and optimize the data lakehouse for performance and reliability.
Apply best practices in data modeling, pipeline design, and query optimization.
Monitor pipeline performance and troubleshoot issues.
Develop and manage ETL/ELT processes for loading data into data warehouses and data lakes.
Maintain data integrity and consistency across systems.
Collaborate with engineering, product, finance, bizops, and other teams to understand data needs and deliver solutions.
Provide data engineering support to ensure data accessibility and usability company-wide.
Define and enforce data quality standards.
Implement data governance to ensure compliance with internal and external policies.
Stay current with industry trends and technologies in data engineering.
Continuously improve data processes and infrastructure for efficiency and scalability.
Document data engineering processes, designs, and architectures