Sr Data Infrastructure Engineer

Cobot

6h•$180,000 - $215,000•Hybrid

About The Position

Are you excited to design data systems that power real-time insights for next-generation robotics products? Do you thrive in fast-paced environments where flexibility, autonomy, and collaboration are key? If you’re passionate about building pipelines that transform high-volume telemetry and 3D sensor data into trustworthy, low-latency insights—and want to shape how humans manage and interact with robotic fleets—we invite you to join us at Cobot as a Senior Data Infrastructure Engineer. This role is ideal for engineers who enjoy working at the intersection of streaming systems, analytics platforms, and mission-critical operations. You’ll collaborate across robotics, AI, and product teams to architect and scale the backbone that powers Vista (our AI insights layer) and ScoutMap (our 3D mapping and environment intelligence system). From streaming ingestion to analytics-ready marts, your work will ensure our customers can ask “what’s happening now?”—whether it’s the state of a robot fleet, the readiness of a facility, or the flow of critical tasks—and always get fast, reliable answers. Collaborative Robotics is a team of innovators and builders redefining the future of human-robot interaction. We are working to realize a world where robots are a trusted extension of your surroundings. They work, adapt, and react around you. Not the other way around. This role is located at our Santa Clara (HQ) or Seattle office. It is also remote-friendly from the San Francisco Bay Area, Seattle, Boston, Denver/Boulder, or Pittsburgh.

Requirements

5+ years of professional experience in data engineering or data infrastructure roles
Strong proficiency in Python and SQL, with the ability to write production-quality, scalable, and well-tested code.
Proven experience designing and operating ingestion pipelines and staging layers (streaming and batch) that support downstream analytics and product use cases.
Experience deploying and managing cloud data infrastructure in AWS using infrastructure-as-code (e.g., Terraform, Kubernetes, Docker).
Hands-on experience with cloud-based data platforms, storage systems, and infrastructure.
Familiarity with data quality practices, testing frameworks, and CI/CD for data pipelines.
Highly motivated teammate with excellent oral and written communication skills.
Enjoy working in a fast paced, collaborative and dynamic start-up environment as part of a small team.
Willingness to travel occasionally for on-site support or testing, as needed.
Must have and maintain US work authorization.

Nice To Haves

Proven experience as the technical owner or lead for a data ingestion platform or foundational data system.
Experience with Databricks (Delta Live Tables, SQL Warehouse) and familiarity with dbt or similar tools to support analytics engineers.
Strong understanding of multi-tenant architectures, with a track record of balancing cost-efficiency, performance, and reliability at scale.
Background in streaming systems (Kafka, Flink, Kinesis, or Spark Structured Streaming).
Familiarity with data quality and observability tools (e.g., Great Expectations, Monte Carlo).
Exposure to IoT/robotics telemetry or 3D/spatial data processing (e.g., point clouds, LiDAR, time-series).
Experience working in a product-facing data role, collaborating directly with product, engineering, and AI/ML teams to deliver data systems that enable new customer-facing features.
Demonstrated ability to translate product requirements into scalable data solutions with measurable business impact.

Responsibilities

Own the full ingestion path from edge to cloud, ensuring robot telemetry, sensor data, and warehouse events are reliably captured, transported, and made available for downstream systems.
Design, build, and operate scalable pipelines and foundational data layers (streaming and batch) that deliver low-latency, reliable data for analytics, AI/ML, and product features.
Build and maintain ingestion pipelines from object storage (e.g., S3) into Databricks, including raw → staged → analytics-ready layers, supporting both streaming and batch workloads.
Own the reliability and CI/CD of the data warehouse and foundational data layers, enabling safe, repeatable deployment of schema changes, transformations, and infrastructure that analytics engineers depend on.
Implement observability, monitoring, and data quality checks to ensure pipeline correctness, detect failures or drift, and maintain trust in data used by Vista, Portal, and Scoutmap.
Scale and optimize multi-tenant data infrastructure, balancing performance, reliability, and cost-efficiency as Cobot’s customer base and data volume grow.
Collaborate directly with robotics, AI/ML, product, and analytics teams to translate product requirements into resilient data systems that unlock customer-facing features.
Establish and enforce best practices for data engineering, reliability, security, and CI/CD across ingestion, staging, and warehouse layers—owning the foundations while enabling analytics engineers to ship metrics, marts, and dashboards efficiently.