Software Engineer

Job description

We’re looking for a Software Engineer who is passionate about building a modern, scalable Data as a Service (DaaS) platform that powers Intuitive’s Digital products and supports over 2,000 engineers across the organization. In this role, you will own and evolve critical components of our real-time and micro-batch data pipelines that power product development, internal tools, and analytics. Your work will focus on enabling high-throughput, low-latency data delivery through streaming pipelines, dynamic transformations, and APIs, driving discoverability, accessibility, and actionable insights.

You will help define the architecture and engineering practices that support self-service analytics and operational decision-making on scale. As a catalyst for change, you will be at the forefront of reimagining how engineering teams consume and interact with data. Long-term success in this role means building robust, efficient systems and replacing legacy processes with modern solutions that allow teams to move faster, with greater confidence and autonomy.

Responsibilities

Design and build scalable, distributed Data as a Service that ingest, process, and serve data from robotics, manufacturing, engineering, and clinical sources in real time and batch modes. Develop and maintain robust APIs, data services, and tooling to provide internal teams with secure, efficient, and intuitive access to high-quality data. Partner with engineering, analytics, and business stakeholders to evolve data contracts and models that support emerging use cases and ensure semantic consistency.

Implement CI/CD practices for data services, including automated testing for data quality, service reliability, and schema evolution. Championing a self-service data culture by building discoverable, well-documented data products and guiding teams toward empowered, autonomous data access. Act as a technical leader within the data domain driving best practices, mentoring teammates, and continuously improving how data is produced, shared, and consumed across the organization.

Qualifications

Qualifications

Key Skills & Experience: Solid quantitative background in Computer Science, Engineering, Physics, Math, or 3+ years of hands-on experience in a technically demanding role. Proficient in at least two major programming languages such as Python, Go, Scala, C++, or Java, with a strong understanding of software design and architecture. Proven experience building data pipelines and working with distributed systems using technologies like Apache Spark, Kafka, Elasticsearch, Snowflake, and Airflow.

Strong collaborator who actively contributes to code reviews, system design discussions, sprint planning, and KPI evaluations to drive team excellence and technical quality. Minimum a bachelor's or master's degree in computer science, information technology, or a related field.

Bonus Points

Experience working on Data Platform or Infrastructure Engineering teams. Hands-on experience with AWS, Docker, Kubernetes, Kafka, Elasticsearch, Apache Airflow, Snowflake, and Terraform. Familiarity with CI/CD best practices for DataOps and deployment automation.

About the Data Services Team

We are a rapidly growing organization made up of Software and Data Engineers, with a strong DevOps culture, focused on building a next-generation Data as a Service (DaaS) platform. We are seeking a strong technical lead who is passionate about designing scalable, real-time data systems that deliver high-quality, trusted data across the company. Our platform leverages technologies such as Apache Kafka for real-time data ingestion, Apache Flink for stream processing and transformation, Snowflake for scalable warehousing, and AWS as our cloud backbone.

We focus on serving data via APIs and event streams to enable on-demand access, analytics, and machine learning across product and engineering teams. This is a newly formed group with a mandate to drive meaningful change by enabling fast, reliable, and consistent access to data. We aim to eliminate organizational silos and foster a strong culture of collaboration and ownership. If you see a high-leverage solution to a long-standing data problem, we support tearing down legacy systems and building the right solution provided you have a strong plan and the drive to make it happen.