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Data Infrastructure Engineer Jobs (NOW HIRING)

Clera

Robotics Data Infrastructure Engineer

Los Angeles, CA · Remote

$117K - $140K/yr

Instrument observability, monitoring, and alerting for data flows and infrastructure. * Collaborate closely with robotics engineers and data scientists to translate platform needs into production ...

New

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

They are seeking an experienced Data Infrastructure Engineer to enhance their AI data infrastructure by building tools and software components for managing robot data and cloud resources.

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

They are seeking an experienced Data Infrastructure Engineer to enhance their AI data infrastructure by building tools and software components for managing robot data and cloud resources.

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

They are seeking an experienced Data Infrastructure Engineer to enhance their AI data infrastructure by building tools and software components for managing robot data and cloud resources.

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

They are seeking an experienced Data Infrastructure Engineer to enhance their AI data infrastructure by building tools and software components for robot data management and supporting AI researchers ...

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$150K - $400K/yr

Our Helix team is looking for an experienced Data Infrastructure Engineer, to take our AI data infrastructure to the next level. This role is focused on building tools and software components that ...

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$150K - $400K/yr

Our Helix team is looking for an experienced Data Infrastructure Engineer, to take our AI data infrastructure to the next level. This role is focused on building tools and software components that ...

Figma

Software Engineer, Data Infrastructure

$117K - $140K/yr

Figma is a company on a mission to make design accessible to all, and they are seeking a Software Engineer for their Data Infrastructure team. This role involves designing and building large-scale ...

zaimler

Data Infrastructure Engineer (Query Engine)

San Mateo, CA · On-site

$130K - $156K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

Our team of seasoned data infrastructure and machine learning experts (from LinkedIn, Visa, Truera ... Advocates for engineering efficiency and continuous improvement. * A leader who enjoys mentoring ...

New

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All JobsData Infrastructure Engineer Jobs

Data Infrastructure Engineer information

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$46.5K

$127.1K

$182K

How much do data infrastructure engineer jobs pay per year?

As of Aug 16, 2026, the average yearly pay for data infrastructure engineer in the United States is $127,066.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,500.00 and $141,000.00 per year, depending on experience, location, and employer.

What is a data infrastructure engineer?

A Data Infrastructure Engineer is a professional who designs, builds, and maintains the systems and architecture that store, process, and manage large volumes of data for organizations. They focus on creating scalable and reliable data pipelines, ensuring data is accessible and secure, and integrating data from various sources. Their work enables data scientists, analysts, and other stakeholders to efficiently use data for decision-making and analytics. Data Infrastructure Engineers often work with tools like Hadoop, Spark, and cloud platforms, and play a critical role in supporting modern data-driven businesses.

What are the key skills and qualifications needed to thrive as a data infrastructure engineer?

To thrive as a Data Infrastructure Engineer, you need a solid background in computer science, experience with database management, and expertise in building and optimizing data pipelines, often supported by a relevant degree. Familiarity with tools and platforms like Hadoop, Spark, SQL, cloud services (AWS, Azure, GCP), and containerization technologies such as Docker and Kubernetes is typically required, alongside certifications in cloud or database technologies. Strong problem-solving skills, attention to detail, and effective communication help you collaborate with cross-functional teams and resolve complex technical challenges. These skills and qualities are crucial for ensuring reliable, scalable, and efficient data systems that support business analytics and decision-making.

What are some typical challenges data infrastructure engineers face when scaling systems to handle increased data volume?

Data Infrastructure Engineers often encounter challenges such as ensuring data pipelines remain reliable and performant as data volume grows. This includes optimizing storage solutions, managing distributed systems, and automating data ingestion and transformation processes. Collaborating closely with data scientists and analysts is key to understanding evolving data requirements and proactively addressing potential bottlenecks. Staying updated with the latest tools and best practices helps engineers build scalable, fault-tolerant infrastructure that supports organizational growth.

What is the difference between Data Infrastructure Engineer vs Data Engineer?

AspectData Infrastructure EngineerData Engineer
Primary FocusBuilding and maintaining data infrastructure, pipelines, and storage systemsDesigning, developing, and optimizing data pipelines and models
Skills & CertificationsCloud platforms, data storage, ETL tools, scriptingSQL, Python, Spark, Hadoop, data modeling
Work EnvironmentData teams, infrastructure teams, cloud environmentsData teams, analytics teams, software engineering
Industry UsageTech, finance, healthcare, any data-driven industryTech, finance, retail, analytics-focused companies

While both roles involve working with data pipelines, Data Infrastructure Engineers focus on building and maintaining the underlying data systems and infrastructure, ensuring data availability and reliability. Data Engineers primarily develop and optimize data pipelines and models for analysis and machine learning. Both roles often collaborate but serve different aspects of data management.

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Data Infrastructure Engineer jobs near you
Infographic showing various Data Infrastructure Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, and 4% Contract. Highlights an 86% Physical, 4% Hybrid, and 10% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.

Robotics Data Infrastructure Engineer

Clera

Los Angeles, CA • Remote

$117K - $140K/yr

Full-time

Posted 3 days ago

New

Job description

About the Role

This role sits at the intersection of robotics and data infrastructure at a well-funded, early-stage robotics company. You'll be responsible for building reliable pipelines and storage systems that make large volumes of robot telemetry and sensor data usable for engineering and ML teams. Your work will directly enable faster iteration and safer robotic systems — from raw sensor ingestion all the way through to training-ready datasets and real-time analytics.

You'll join a cross-functional team of robotics engineers, software engineers, and data scientists in a fast-paced, on-site environment in Los Angeles, CA. This is a high-impact, hands-on role with broad scope at a company building at the frontier of physical AI and robotics.

Please note: Visa sponsorship is not available for this role.

What You'll Do

  • Design and build scalable data pipelines to ingest and process robot telemetry and sensor data (camera, LiDAR, IMU, and more).

  • Implement storage solutions and schemas that support analytics, model training, and data replay.

  • Ensure data quality, validation, and lineage across ingestion and transformation stages.

  • Optimize latency and throughput for both real-time and batch processing use cases.

  • Instrument observability, monitoring, and alerting for data flows and infrastructure.

  • Collaborate closely with robotics engineers and data scientists to translate platform needs into production-grade implementations.

  • Productionize ETL/ELT workflows with CI/CD and automated testing.

  • Troubleshoot and resolve production incidents affecting data availability or correctness.

What We're Looking For

Required:

  • 3+ years of hands-on experience building data infrastructure or engineering pipelines specifically for robotics sensor data — this is a dealbreaker requirement.

  • Proven experience designing, building, and maintaining data ingestion, processing, and storage pipelines for sensor data (e.g., camera, LiDAR, IMU).

  • Strong fundamentals in distributed systems, databases, and data pipeline design.

  • Proficiency in Python and/or C++ for building data tooling and pipelines.

  • Hands-on experience with cloud data platforms and distributed processing tools — e.g., AWS or GCP, Kafka or Pub/Sub, Spark or Flink, Airflow.

  • Experience with containerization and deployment of data pipelines using Docker and Kubernetes, plus basic CI/CD.

  • Experience with time-series databases and telemetry data management in a robotics context.

  • Strong communication skills and a collaborative mindset for working across engineering and ML teams.

Nice to Have:

  • Experience with ROS / ROS2 robotics middleware.

  • Familiarity with ML workflow tooling such as MLFlow or Kubeflow for end-to-end robotics data pipelines.

  • Experience with robotics simulation tools (e.g., Gazebo) and synthetic data generation.

  • Prior experience in an early-stage or high-growth startup environment.

Location

This is a full-time, on-site role based in Los Angeles, CA. Candidates based in or willing to relocate to the Los Angeles area are strongly preferred. The company also has a presence in New York City, NY and San Francisco, CA.

Compensation & Benefits

Compensation will be competitive and commensurate with experience, including equity participation appropriate for an early-stage company. Specific details will be shared during the interview process.