IMPORTANT: Suppliers should not submit workers whose physical residence is within the following states due to Intuitive tax and operating entity structure:
Alabama, Arkansas, Delaware, Florida, Indiana, Iowa, Louisiana, Maryland, Mississippi, Missouri, Oklahoma, Pennsylvania, South Carolina, and Tennessee.
Please interpret this as Intuitive policy to which all suppliers are required to comply.

Responsibilities
Build highly scalable distributed systems that leverage event-based and streaming data pipelines to handle ingestion and processing of robot, manufacturing, and clinical data
Enable users by providing self-service APIs and applications to access and interact with data
Work closely with core engineering teams to consistently evolve data models based on growing business needs
Apply software development best practices such as CI/CD, automated testing, infrastructure-as-code, and microservice architectures
Effectively participate in the team s planning, code reviews, KPI reviews, and design discussions leading to continuous improvement in these areas.



Qualifications
Skills, Characteristics, and Technology:
Exceptional quantitative background (Computer Science, Math, Physics, and/or Engineering), or at least 5+ years of industry experience in a quantitative role
Fluent coding with Python and Java
Proven experience building data pipelines and working with distributed systems using technologies such as Kafka/Flink, Snowflake, AWS Lambdas
Excellent written and verbal communication skills
Proven understanding of best engineering practices, such as unit testing and integration testing, and deployment patterns.
Experience with Kubernetes
Experience with SQL and relational databases
Ability and enthusiasm to work collaboratively and cross-functionally, and take end-to-end ownership to deliver results for customers

Bonus points:
Experience on a Platform team
Experience with Gitlab CI/CD or other CI tooling
Experience with Apache Iceberg
Experience with Terraform and general IaC best practices
You re familiar with CNCF projects and have successfully used them in the past