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Data Infrastructure Engineer Jobs (NOW HIRING)

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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 ...

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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 ...

OpenAI

Software Engineer, Data Infrastructure

Seattle, WA · On-site

$150 - $210/hr

Have 4+ years in data infrastructure engineering OR * Have 4+ years in infrastructure engineering with a strong interest in data * Take pride in building and operating scalable, reliable, secure ...

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 ...

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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 Sep 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?

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, 85% Full Time, 11% Part Time, and 3% Contract. Highlights an 86% Physical, 3% Hybrid, and 11% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.

Staff Software Engineer, Data Infrastructure

Peregrine

San Francisco, CA • On-site

$134K - $162K/yr

Full-time

Re-posted 23 days ago

Job description

Job Summary:
Peregrine is a company backed by leading Silicon Valley investors, focused on helping public safety organizations and governments address societal challenges with their AI-enabled platform. The Staff Data Infrastructure Engineer will have deep ownership over the data layer, architecting and building systems to manage massive volumes of operational data for critical decision-making.
Responsibilities:
• Designing and operating a high-throughput, real-time data integration platform across diverse customer environments
• Architecting a scalable open table format layer for reliable data storage at petabyte scale
• Building and optimizing distributed data processing pipelines with Apache Spark and adjacent streaming technologies
• Driving performance, reliability, and cost efficiency across the full data infrastructure stack
• Collaborating with platform and product engineering teams to define data contracts, schemas, and integration patterns
• Establishing best practices, tooling, and patterns that raise the quality bar for data infrastructure across the organization
Qualifications:
Required:
• 8+ years of experience architecting and operating large-scale data infrastructure systems in production environments
• Deep expertise with open table formats, particularly Apache Iceberg — including schema evolution, partitioning strategies, compaction, and time travel
• Extensive hands-on experience with Apache Spark for batch and streaming data processing at scale
• Strong background in real-time data integration and stream processing, leveraging technologies such as Apache Kafka, Apache Flink, or equivalents
• Solid experience with data pipeline orchestration using Airflow or similar tools
• Strong software engineering fundamentals in Python and/or Scala, with a track record of writing production-quality code
• Extensive experience with AWS or comparable cloud platforms, including S3-based data lake architectures
• Experience with Kubernetes and containerized deployment of data workloads
• Degree in Computer Science, Engineering, or a related field, or equivalent practical experience
Company:
Context changes everything. Founded in 2018, the company is headquartered in San Francisco, USA, with a team of 201-500 employees. The company is currently Growth Stage.

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