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Data Infrastructure Engineer Jobs (NOW HIRING)

Figure

Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$150K - $350K/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

San Francisco, CA · On-site +1

$134K - $162K/yr

Identify and drive cost optimization opportunities across data processing, compute infrastructure, and storage. * Collaborate with AI researchers, data scientists, product engineers, and business ...

Stellic

Lead Data Infrastructure Engineer

San Mateo, CA · On-site +1

$230K - $260K/yr

Stellic is growing! We're looking for a Lead Data Engineer to own the next phase of our data ... Infrastructure: Terraform * Data transforms: dbt * Orchestration: MWAA / Airflow * Storage: S3 ...

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Data Infrastructure Engineer information

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How much do data infrastructure engineer jobs pay per year?

As of Jul 6, 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, and why are they important?

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.

More about Data Infrastructure Engineer jobs
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Data Infrastructure Engineer jobs near you
Helix AI Engineer, Data Infrastructure

Helix AI Engineer, Data Infrastructure

Figure

San Jose, CA • On-site

$150K - $350K/yr

Full-time

Posted 3 days ago

Job description

Figure is an AI Robotics company developing a general purpose humanoid. Our humanoid robot is designed for commercial tasks and the home. We are based in San Jose and require 5 days/week in-office collaboration. It's time to build.
Figure's vision is to deploy autonomous humanoids at a global scale. 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 offload, store, manipulate and provide access to robot data, managing on premise and cloud resources, and providing production support for this infrastructure. The ideal candidate has experience building tools and infrastructure for a large-scale autonomous / deep learning system.
Responsibilities
  • Design, build, and maintain tools and software components that offload, store, manipulate and provide access to robot data
  • Architect and manage storage and compute resources across on-premise and cloud environments at a massive scale.
  • Implement optimal data transmission and storage solutions for all stages of Figure's data pipeline from recording to neural network training
  • Work together with AI researchers to support new kinds of data workflows

Requirements
  • Strong software engineering fundamentals
  • Bachelor's or Master's degree in Computer Science, Robotics, Engineering, or a related field
  • Minimum of 4 years of professional, full-time experience building reliable backend systems
  • Experience with Linux and command line tools
  • Experience with Python
  • Experience using and managing data stores (Postgres, MySQL, ElasticSearch, Redis, etc.).

Bonus Qualifications
  • Experience managing cloud infrastructure (AWS, Azure, GCP)
  • Experience with job scheduling / orchestration tools (SLURM, Kubernetes, LSF, etc.)
  • Experience with configuration management tools (Ansible, Terraform, Puppet, Chef, etc.)
  • Experience building data annotation and dataset management tools.

The US base salary range for this full-time position is between $150,000 - $350,000 annually.
The pay offered for this position may vary based on several individual factors, including job-related knowledge, skills, and experience. The total compensation package may also include additional components/benefits depending on the specific role. This information will be shared if an employment offer is extended.

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