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Data Infrastructure Engineer Jobs (NOW HIRING)

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Helix AI Engineer, Data Infrastructure

San Jose, CA · On-site

$126K - $165K/yr

They are seeking an experienced Data Infrastructure Engineer to enhance their AI data infrastructure by building tools and software components for managing robot data and cloud resources.

IEX Group

Senior Data Infrastructure Engineer

New York, NY · On-site

$200K - $250K/yr

Senior Data Infrastructure Engineer IEX (IEX Group, Inc.) is an exchange operator and technology company dedicated to innovating for performance in capital markets. Founded in 2012, IEX launched a ...

IEX Group

Senior Data Infrastructure Engineer

New York, NY · On-site

$200K - $250K/yr

Senior Data Infrastructure Engineer IEX (IEX Group, Inc.) is an exchange operator and technology company dedicated to innovating for performance in capital markets. Founded in 2012, IEX launched a ...

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 ...

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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 Jun 10, 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
What cities are hiring for Data Infrastructure Engineer jobs? Cities with the most Data Infrastructure Engineer job openings:
What states have the most Data Infrastructure Engineer jobs? States with the most job openings for Data Infrastructure Engineer jobs include:
What job categories do people searching Data Infrastructure Engineer jobs look for? The top searched job categories for Data Infrastructure Engineer jobs are:
Data Infrastructure Engineer jobs near you
Infographic showing various Data Infrastructure Engineer job openings in the United States as of June 2026, with employment types broken down into 1% As Needed, 98% Full Time, and 1% Contract. Highlights an 87% Physical, 5% Hybrid, and 8% Remote job distribution, with an average salary of $127,066 per year, or $61.1 per hour.
Sr. Data Infrastructure Engineer

Sr. Data Infrastructure Engineer

Evolv Technologies Inc.

Waltham, MA • On-site

$123K - $148K/yr

Full-time

Medical, Dental, Vision, Retirement, PTO

Posted 12 days ago

Job description

The Elevator Pitch
Join Evolv as Senior Data Infrastructure Engineer in the Machine Learning & Sensors organization, responsible for building and operating the scalable, secure, and reliable data pipelines that power our AI/ML research and production systems. In this role, you will own the end-to-end data lifecycle-from collection on thousands to millions of edge devices, through cloud ingestion and processing, into a centralized data factory enabling model training, evaluation, and continuous improvement.
Data is the backbone of our mission to deliver best-in-class AI-based weapon detection systems. You will ensure that data flows seamlessly across geographies, devices, and cloud systems while meeting strict requirements for quality, privacy, security, and scale. This role is ideal for someone who thrives at the intersection of distributed systems, cloud pipelines, and ML-driven data needs.
Success in the Role: What performance outcomes will you work toward in the first 6-12 months?
In the first 30 days:
  • Develop a deep understanding of existing edge-to-cloud data pipelines and deployment environments.
  • Review current data ingestion flows, governance policies, and cloud infrastructure.
  • Assess pain points in data reliability, quality, and operational scalability.
  • Build relationships with AI/ML, data science, field operations, and cloud engineering teams.
  • Design and prototype data processing pipelines (both cloud and edge)

Within the first three months:
  • Design and implement improvements to core ingestion, validation, and processing pipelines.
  • Deploy scalable data pipeline with AWS-based components (S3, EC2, Lambda, Glue, Step Functions, SageMaker integrations).
  • Introduce automated validation workflows to detect corruption, missing metadata, or malformed data.
  • Design and implement automated model evaluation, model training and model improvement pipeline to speed up experiments
  • Partner with field operations to improve data reliability, observability, and coverage across deployments.

By the end of the first year:
  • Own the entire lifecycle of mission-critical data pipelines supporting AI/ML research and production.
  • Architect next-generation edge-to-cloud data systems that scale across millions of devices.
  • Define and enforce data governance frameworks including retention, access control, privacy, and lineage.
  • Enable ML teams to rapidly experiment through high-quality, discoverable, versioned datasets.

The Work: What type of work will you be doing? What assignments, requirements, or skills will you be performing on a regular basis?
End-to-End Data Pipeline Ownership:
  • Design, build, and maintain both research and production data pipelines spanning edge devices, cloud services, and centralized data platforms.
  • Own the full data lifecycle: collection, ingestion, processing, obfuscation, versioning, access, retention, and retirement.
  • Edge-to-Cloud Data Flow:
  • Develop resilient ingestion pipelines capable of handling variable connectivity and device heterogeneity.
  • Support secure data transfer from the field to cloud storage systems.
  • Collaborate with field ops to enhance data coverage, observability, and operational robustness.
  • Data Quality, Governance & Compliance:
  • Implement privacy-preserving transformations and obfuscation pipelines.
  • Build automated cleaning/validation steps to remove duplicates, detect corruption, and validate metadata.
  • Establish data lineage, retention policies, and access controls ensuring compliance and traceability.

Data Services for AI/ML:
  • Provide scalable data services for model training, evaluation, and research experimentation.
  • Support continuous data refresh and retraining workflows.
  • Integrate with data labeling services and annotation workflows.
  • Enable efficient access patterns for large-scale ML workloads.

AWS-Based Cloud Infrastructure:
  • Build and optimize pipelines using AWS services (S3, EC2, SageMaker, Lambda, Glue, Step Functions).
  • Design for cost-efficiency, performance, and reliability at scale.

Collaboration & Feedback Loops:
  • Partner with AI/ML engineers, scientists, and data scientists to understand data requirements.
  • Translate feedback into automated improvements in data collection, labeling, and consumption.
  • Support cross-functional teams in exploratory analysis and debugging data issues.

Scaling the Data Factory:
  • Design and manage data schema, data versioning and data factory updates
  • Architect systems that scale globally across millions of devices.
  • Ensure the data platform remains flexible for research and reliable for production operations.

Qualifications:
Minimum Qualifications:
  • Bachelor's or Master's degree in Computer Science, Data Engineering, Software Engineering, or related field.
  • 2-3+ years of experience building production data pipelines and data platforms that support AI/ML models.
  • Strong proficiency in Python, C++ and distributed data processing frameworks.
  • Hands-on experience with AWS services including S3, EC2, SageMaker, and Glue.
  • Experience designing data systems that support large-scale ML training and experimentation.
  • Knowledge of data governance, access control, and lifecycle management.
  • Experience collaborating with ML, data science, operations, and cloud teams.

Preferred Qualifications:
  • Experience building pipelines spanning edge devices and cloud systems.
  • Background working with large-scale sensor, image or IoT data.
  • Familiarity with data labeling tools and annotation workflows.
  • Experience implementing dataset versioning, lineage, and reproducibility systems.
  • Understanding of privacy, compliance, or regulated data environments.
  • Experience supporting global, multi-region data platforms.

Example Problems You Will Own
  • Design a resilient global ingestion pipeline aggregating sensor data from millions of devices.
  • Build ML-ready data services enabling easy discovery, versioning, and consumption of datasets.
  • Implement automated validation and cleaning workflows that dramatically reduce bad data.
  • Define and enforce lifecycle and governance policies across research and production datasets.

What is leadership like for this role? What is the structure and culture of the team?
  • You will join our R&D organization, reporting directly to VP of ML and sensors. In this role, you will interface with cross-disciplinary teams of highly skilled and autonomous engineers with expertise in Electromagnetics, Computer Vision, and AI. Our R&D organization includes more than 100 dedicated developers, engineers, scientists, managers and directors, each bringing deep technical knowledge and a strong culture of collaboration and support.
  • The team culture is one based on building trust, collaboration, on-going development through kindness, authenticity, courage, drive, and fun!

Where is the role located? This role is based at our headquarters in Waltham, Massachusetts. Due to the nature of our software-enabled hardware products, this position requires a minimum of 60% or 3 days per week on-site work.
**Candidates MUST live within commutable distance to our office in Waltham, MA**
What is the salary range? The base salary range for this full-time position is $129,000 - $209,000. Our salary ranges are determined by role, level, and location. The range displayed on each job posting reflects the minimum and maximum target for new hire salaries for the position across all US locations. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training. Your recruiter can share more about the specific salary range for your preferred location during the hiring process.
• Please note that the compensation details listed in role posting reflect the base salary only, and do not include commission, equity, or benefits
Benefits
At Evolv, we're on a mission to help make public spaces safer through innovative security technology. So, we're looking for future teammates who embody our values, people who:
  • Do the right thing, always;
  • Put people first'
  • Own it;
  • Win together; and continue to
  • Be bold, stay curious.

Our Benefits Include:
  • Equity as part of your total compensation package
  • Medical, dental, and vision insurance
  • Health Savings Account (HSA)
  • A 401(k) plan (and 2% company match)
  • Flexible Paid Time Off (PTO)- take the time you need to recharge, with manager approval and business needs in mind
  • Quarterly stipend for perks and benefits that matter most to you
  • Tuition reimbursement to support your ongoing learning and development
  • Subscription to Calm

Evolv Technology ("Evolv") is an Equal Opportunity Employer and prohibits discrimination and harassment of any kind. We welcome and encourage diversity in the workplace, and all employment decisions are made without regard to race, color, religion, national, social or ethnic origin, sex (including pregnancy), age, disability, HIV Status, sexual orientation, gender identity and/or expression, veteran status, or any other status protected by law in the locations where we operate. Evolv will not tolerate discrimination or harassment based on any of these characteristics.
Evolv is committed to offering an inclusive and accessible experience for all job seekers, including individuals with disabilities. If you need a reasonable accommodation as part of the job application process, please connect with us at careers@evolvtechnology.com.
Evolv participates in E-verify for all employees after the completion of Form I-9.

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