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Data Infrastructure And Analytics Jobs (NOW HIRING)

XPENG

Senior Data Infrastructure Engineer

Santa Clara, CA · On-site

$127K - $173K/yr

As a Data Infrastructure Engineer, your role is to identify corner cases in autonomous driving ... Monitor key efficiency metrics in edge case resolving process, analyze root causes of changes and ...

Guidehouse

Data Infrastructure Engineer

$117K - $140K/yr

Build and optimize ETL/ELT pipelines to produce curated, analytics-ready datasets for reporting and ... Data Pipelines * Implement automated cloud provisioning in AWS using Infrastructure as Code (IaC ...

Guidehouse

Data Infrastructure Engineer

Build andoptimize ETL/ELT pipelines to produce curated, analytics-ready datasets for reporting and ... Data Pipelines * Implement automated cloud provisioning in AWS using Infrastructure as Code (IaC ...

Sequoia Connect

Data Infrastructure Architect

Atlanta, GA · Remote

$65.25 - $84/hr

We are currently searching for a Data Infrastructure Architect: The Challenge (Responsibilities ... Lead troubleshooting of production issues and drive root cause analysis and corrective actions.

Figma

Software Engineer - Data Infrastructure

San Francisco, CA · On-site

$134K - $162K/yr

The Data Platform team at Figma builds and operates the foundational systems that power analytics ... In the coming years, we're focused on building the data infrastructure layer for Figma's AI-powered ...

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Data Infrastructure And Analytics information

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$153K

$178.7K

$202K

How much do data infrastructure and analytics jobs pay per year?

As of Aug 16, 2026, the average yearly pay for data infrastructure and analytics in the United States is $178,749.00, according to ZipRecruiter salary data. Most workers in this role earn between $165,500.00 and $191,500.00 per year, depending on experience, location, and employer.

What does data infrastructure and analytics mean?

Data infrastructure and analytics refer to the systems, tools, and processes used to collect, store, manage, and analyze data within an organization. Data infrastructure includes databases, data warehouses, and cloud platforms, while analytics involves examining data to generate insights, often using software like SQL, Python, or BI tools. Professionals in this field need strong technical skills and understanding of data management principles.

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

AspectData Infrastructure And AnalyticsData Engineer
Primary FocusBuilding data systems, managing data infrastructure, and enabling analyticsDesigning, constructing, and maintaining data pipelines and storage solutions
Skills & CertificationsData management, SQL, cloud platforms, analytics toolsProgramming (Python, Java), ETL, database systems, cloud services
Work EnvironmentCollaborates with data analysts, scientists, and business teamsWorks closely with data architects and software engineers
Industry UsageUsed across industries for data-driven decision makingPrimarily in tech, finance, and large enterprises with complex data needs

While both roles involve working with data systems, Data Infrastructure And Analytics focuses on creating and managing the infrastructure that enables data analysis, whereas Data Engineers primarily build and maintain the data pipelines and storage solutions that support these analytics. Understanding these distinctions helps organizations assign the right skills to each role.

Is data infrastructure and analytics a well paid job?

Data infrastructure and analytics roles are generally well paid, especially for professionals with strong skills in database management, cloud platforms, and data modeling. Salaries vary based on experience, location, and certifications, but these jobs tend to offer competitive compensation within the tech industry.
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Data Infrastructure And Analytics jobs near you
Infographic showing various Data Infrastructure And Analytics 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 $178,749 per year, or $85.9 per hour.

Senior Software Engineer, Data Infrastructure

Roblox

San Mateo, CA • On-site

$123K - $168K/yr

Full-time

Re-posted 23 days ago

Job description

Roblox's data infrastructure processes petabytes of data daily, powering analytics, ML, and product decisions. As a Senior Software Engineer in our Data Infra org, you will design, build, and scale the distributed data infrastructure platforms that power Roblox. You will own and drive the next-generation architecture of our core platforms, which span Kafka, Flink, Spark, Trino, Druid, Airflow and Data Catalog. This role combines high ambiguity and ownership to push the boundaries of what our infrastructure can handle at massive scale, giving you the unique opportunity to steer the evolution of the data landscape.

You Will:
  • Own and Scale Core Platform Components: Take responsibility for the design, architecture, and implementation of 1-2 key data platform frameworks within our stack
  • Collaborate and Align: Partner with infra, data science, and product engineering teams to ensure your target platform's capabilities are directly guided by platform governance and product requirements.
  • Optimize Performance at Scale: Dive deep into engine internals, query planning, state management, memory optimization, serialization efficiency to maximize throughput and reliability under heavy load.
  • Drive Infrastructure Robustness: Lead the design, testing, and operational lifecycle of next-generation infrastructure features running on Kubernetes across cloud environments.
  • Agentic Interface: Embed AI/ML capabilities within our data platforms, leveraging LLMs for data discovery and generation, building autonomous, self-serve mechanisms for platform interaction layer.
You Have:
  • 5+ years of experience building, designing, testing and maintaining production-grade, large-scale distributed systems.
  • Data Platform Depth: Deep technical experience building or strong familiarity with at least 1 or 2 foundational technologies within our stack: Kafka, Flink, Spark, Trino, Druid, Airflow, or Data Catalog/Metadata systems.
  • Strong Engineering Foundations: Robust proficiency in Java, Go, or Scala, with a track record of writing clean, highly performant backend code.
  • Cloud Fluency: Experience operating and troubleshooting data infrastructure at scale on top of Kubernetes in AWS or GCP.
  • Technical Leadership: Demonstrated ability to influence technical direction across teams, mentor engineers, and drive alignment on complex cross-cutting initiatives.
  • B.S. equivalent in CS or sufficient experience.
Nice to Have:
  • Contributions to open-source projects in the data infrastructure ecosystem.
  • Experience operating infrastructure at consumer-internet scale (100M+ users).

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