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Data Infrastructure Engineer Jobs (NOW HIRING)

Instacart

Staff Software Engineer, Data Infrastructure

OR · Remote

$114K - $137K/yr

You'll collaborate closely with engineering leadership and stakeholders across Data Science, ML Platform, Ads Infrastructure, Finance Engineering, Product Engineering, and Security. You'll operate ...

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 ...

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 ...

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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 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
Staff Software Engineer, Data Infrastructure

Staff Software Engineer, Data Infrastructure

Slack

San Francisco, CA • On-site

$134K - $162K/yr

Full-time

Posted 22 days ago

Job description

Job Summary:
Salesforce is the #1 AI CRM, where innovation is a way of life. Slack is seeking a Staff Software Engineer to join the Data Infrastructure team, responsible for building secure and scalable data infrastructure that powers analytics and data-driven decision-making. The role involves designing, developing, and operating core data services while ensuring uptime, reliability, and performance.
Responsibilities:
• Design, build, and operate reliable and scalable data infrastructure powering Slack’s analytics, ML, and data-driven decision-making.
• Serve as DRI for multiple core data services specifically on our analytics infrastructure (e.g., StarRocks, Pinot, and Trino), ensuring uptime, reliability, and our compute and orchestration services (e.g., Airflow, Temporal, EMR, Hive Metastore).
• Drive improvements in security, cost efficiency, and developer experience across our data infrastructure.
• Build automation and self-service tools that empower our team and other data teams to easily adopt and manage data workflows.
• Collaborate closely with data engineering, platform, and security teams to design scalable, well-governed solutions.
• Build and enhance our observability, monitoring, and alerting on our services via Grafana and related tooling.
• Partner with other staff and senior engineers to define best practices, technical standards, and support models for Slack’s data ecosystem.
• Mentor and coach other engineers, modeling ownership, collaboration, and operational excellence.
Qualifications:
Required:
• U.S. Citizenship or Permanent Residency. We are unable to provide visa sponsorship for this role.
• 10+ years of software, platform, or infrastructure engineering experience, including time spent supporting data-intensive systems or data platforms.
• Excellent communication skills and the ability to collaborate across cross-functional teams.
• Proven experience in building, deploying, and operating distributed infrastructure at scale.
• Strong technical background with big data and infrastructure technologies — such as Pinot, StarRocks, Trino, EMR, Airflow, Hive Metastore, Kubernetes, or equivalent systems.
• Proficiency in Python, Golang, Bash, and SQL.
• Proficiency with CI/CD (GitHub Actions), Vault, Terraform, Chef, and Grafana.
• Deep understanding of infrastructure reliability, observability, and cost efficiency principles.
• Hands-on experience supporting data pipelines or data engineering workflows is a strong plus.
• A strong sense of ownership and a drive to deliver high-impact, autonomous results.
Company:
Slack is a cloud-based communication and collaboration platform for teams. It is a sub-organization of Salesforce. Founded in 2009, the company is headquartered in San Francisco, USA, with a team of 1001-5000 employees. The company is currently Late Stage.

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