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Data Infrastructure Engineer Jobs (NOW HIRING)

Digitalturbine

Senior Infrastructure Engineer

Durham, NC

$104K - $142K/yr

Implement MLOps and data-pipeline capabilities - training, deployment, and serving infrastructure ... or back-end engineering, with a track record of building robust distributed systems * Deep ...

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Keel Infrastructure

OT Infrastructure Engineer

New York, NY · On-site

$160K - $250K/yr

The OT Infrastructure Engineer owns the full lifecycle of the OT systems - from architecture and ... Own the architecture, design, and full lifecycle of OT systems across all Keel's data center and ...

Virio

Infrastructure Engineer

San Francisco, CA · On-site

$170K - $300K/yr

Infra Engineer Location: San Francisco (in-person, in-office, full-time ONLY) Compensation: $170K ... Own our data infrastructure : databases, data flows, and the systems that keep them performant ...

Arlo

Senior ML Infrastructure Engineer

Manhattan, NY · On-site

$119K - $162K/yr

As a Senior ML Infrastructure Engineer, you will own the data infrastructure that supports underwriting, claims, and operational workflows, ensuring reliable and scalable data pipelines for the ...

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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.

Data/ML Infrastructure Engineer

Matter Intelligence

San Francisco, CA • On-site

$126K - $166K/yr

Full-time

Medical, Dental, Vision

Re-posted 21 days ago

Job description

About the Role
We are seeking a Data Infrastructure Engineer to build and operate the infrastructure that turns drone, aerial, and orbital sensing data into production datasets, models, and customer-facing insights. This role spans ingestion, processing, storage, compute, and serving, with a strong emphasis on reliability, observability, performance, and cost.
You will work closely with research and product engineering to shorten iteration cycles, improve reproducibility, and raise the quality bar for production systems. You will define clear interfaces and operational standards that keep the platform trustworthy as data volume, model complexity, and product usage scale.
What You'll Do
  • Design, build, and operate scalable data and ML infrastructure on AWS, including workloads running on Kubernetes
  • Build and maintain systems for ingestion, processing, storage, and serving, with strong guarantees around data quality, correctness, and operational safety
  • Partner closely with research to support perception model training and evaluation workflows, enabling faster experimentation and more reproducible iteration
  • Build platform primitives for observability, data versioning, lineage, evaluation, reproducibility, and operational excellence
  • Partner with product engineering to ensure data- and model-derived insights are accessible through reliable, low-latency serving and retrieval interfaces
  • Design systems that enable efficient access patterns for customer-facing products, including search, indexing, and large-scale querying
  • Identify and address bottlenecks in throughput, cost, and operational complexity as the platform scales

What We're Looking For
You have strong software engineering fundamentals and have built production systems where reliability, cost, and performance matter. You can reason clearly about distributed systems tradeoffs, and you have experience designing data-intensive infrastructure that other engineers depend on.
You are comfortable working across data platform and ML platform concerns, and you understand how tightly coupled they become in production. You care about reproducibility, debuggability, and developer experience because you have seen how quickly they become bottlenecks.
You work effectively across research and product teams. You can translate ambiguous needs into clear interfaces and systems, and you can drive work from design through production while maintaining a high quality bar.
A few things we expect in this role:
  • Meaningful experience building production data infrastructure, ML infrastructure, or distributed systems
  • Strong programming skills in Python and SQL, with the judgment to choose the right abstractions and interfaces for production systems
  • Experience building and operating systems on AWS
  • Familiarity with modern infrastructure and platform tooling, including Kubernetes, Docker, and Terraform
  • Experience working with production storage and serving systems such as Postgres and Redis
  • Familiarity with data and ML workflow tooling such as Metaflow
  • Strong instincts for observability, testing, and operational excellence

Nice to Have
  • Experience supporting ML training, evaluation, batch inference, or model deployment in production
  • Familiarity with modern large-scale data patterns and tooling, including streaming, backfills, partitioning strategy, and schema evolution
  • Experience building internal platform primitives such as data versioning and lineage, dataset curation, experiment tracking, or tooling for reproducible workflows
  • Exposure to perception, multimodal, or geospatial systems, especially where data originates from real sensors and is used in real products

Location
This is a full-time role based in San Francisco, CA.
ITAR Requirements
To comply with U.S. export regulations, applicants must be one of the following:
  • A U.S. citizen or national
  • A lawful permanent resident (green card holder)
  • Eligible to obtain required authorizations from the U.S. Department of State

Employee Offerings & Benefits
At Matter, we believe in rewarding high performance and providing the support you need to thrive. Our compensation and benefits package includes:
  • Competitive compensation based on experience
  • Early-stage equity package
  • 100% employer-paid health, dental, and vision coverage
  • Opportunity to work on novel sensing, data, and AI systems with real-world deployment paths across drone, aerial, and orbital platforms

Who You Are
You are a strong engineer who likes building reliable systems that other teams can trust. You care about infrastructure quality, operational rigor, and clear interfaces. You are energized by working close to the data, close to the models, and close to the product.

Apply