Data Engineer
Path Robotics is hiring a Data Engineer (ML Ops) in Columbus, Ohio to architect its data platform, building data models, pipelines, and data-quality monitoring that let ML and robotics teams train, validate, and deploy AI models. The core stack is Python, SQL, and a modern data stack such as AWS, Snowflake, Dagster, and dbt.
- Compensation
- Not disclosed
- Location
- Columbus, Ohio
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 2+ years
- Category
- Data engineering
- Posted
Skills
Must have
- 2+ years of experience in data engineering or a closely related role, building and supporting production data pipelines
- Strong proficiency in SQL and Python
- Hands-on experience with tools like AWS, Snowflake, Dagster, dbt, or an equivalent modern data stack
- Solid grasp of data modeling, warehousing concepts, and pipeline orchestration
- Curious about the problems your data solves; enjoy working directly with stakeholders to deliver data that drives real decisions
- Take ownership of your work, communicate clearly, and want to learn from and contribute to an experienced team
- Ability to comply with U.S. export control regulations (ITAR and/or EAR), which may include meeting the definition of a 'U.S. Person' where required
- Successful completion of a background check consistent with applicable requirements (employment may be contingent on this)
Nice to have
- Experience working with sensor data, robotics logs, manipulation data, or other high-volume, real-world datasets
Full description
Build the Path Forward
At Path Robotics, we’re building the future of embodied intelligence. Our AI-driven systems enable robots to adapt, learn, and perform in the real world closing the skilled labor gap and transforming industries. We go beyond traditional methods, combining perception, reasoning, and control to deliver field-ready AI that is risk-aware, reliable, and continuously improving through real-world use.
Big, hard problems are our everyday work, and our team of intelligent, humble, and driven people make the impossible possible together.
You will play a mission-critical role in architecting our data platform, enabling our machine learning and robotics teams to move faster, experiment more effectively, and deploy AI solutions into production.
This is an opportunity to push our data infrastructure forward and drive value across the business. Reporting to the Data Engineering Manager, you will collaborate with a talented team of data engineers, machine learning engineers and software engineers to deliver clean, reliable, and scalable data products that fuel experimentation, production models, and cutting-edge AI development.
What You’ll Do
- Partner with software, ML, robotics, and analytics teams to understand their data needs and turn them into well-modeled tables and data products.
- Develop and extend data models and pipelines assets that support experimentation, model training and validation, reporting, and business decisions.
- Model large-scale, multi-modal data such as sensor streams, application telemetry, system logs, and unstructured blob data. Identify opportunities for this data to support our mission.
- Monitor pipeline health and data quality, troubleshoot failures, and improve tests and alerting in order to build trust in our data assets.
- Apply and strengthen the team's standards for governance, lineage, documentation, and data contracts.
- Write clean, tested, well-documented Python and SQL, and take part in team code reviews.
- Improve the data platform's usability, performance, and tooling, and evangelize enhancements among our user base.
Who You Are
- 2+ years of experience in data engineering or a closely related role, building and supporting production data pipelines.
- Strong proficiency in SQL and Python.
- Hands-on experience with tools like AWS, Snowflake, Dagster, dbt, or an equivalent modern data stack.
- Solid grasp of data modeling, warehousing concepts, and pipeline orchestration.
- Solid grasp of data modeling, warehousing concepts, and pipeline orchestration.