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Mainframe Data Engineer Jobs (NOW HIRING)

Argyllinfotech

Test Data Architect

Minneapolis, MN · On-site

$66.50 - $85.50/hr

Perform data discovery, analysis, profiling, and data mining across databases, files, mainframe ... Collaborate across architecture, engineering, QA, operations, product, and business teams. Key ...

Omm IT Solutions

Mainframe Computer Operator

Washington, DC · On-site

$55.25 - $71/hr

Location:\-Onsite at US GPO Data Center, Washington, DC \n \n * Shift\-based work:\- \n \n \n \n \n ... Load and unload paper, toner, and developer; ensure proper supply management. \n \n \n * Remove ...

PNC Financial Services Group, Inc.

$36.25 - $46.50/hr

Proficiency with File-AID for testing, data analysis, file manipulation, and production support ... Mainframe Programming, Process Improvements, Release Management, Software Solutions, User ...

PNC Bank

$52 - $66.75/hr

Proficiency with File-AID for testing, data analysis, file manipulation, and production support ... Mainframe Programming, Process Improvements, Release Management, Software Solutions, User ...

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Mainframe Data Engineer information

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How much do mainframe data engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for mainframe data engineer in the United States is $129,716.00, according to ZipRecruiter salary data. Most workers in this role earn between $114,500.00 and $137,500.00 per year, depending on experience, location, and employer.

What is a mainframe data engineer?

Mainframe data engineers are IT professionals who specialize in managing, maintaining, and optimizing data systems on mainframe computers. They develop and support data pipelines, databases, and storage solutions, ensuring the efficient processing and integrity of large-scale enterprise data. Their work often involves programming in languages like COBOL, SQL, and working with technologies such as IBM z/OS and DB2. Mainframe data engineers play a critical role in industries like banking, insurance, and government, where mainframes are still widely used for high-volume transaction processing.

What are the key skills and qualifications needed to thrive as a mainframe data engineer?

To thrive as a Mainframe Data Engineer, you need a solid background in computer science, expertise in mainframe technologies (such as COBOL, JCL, and DB2), and experience with data management concepts. Familiarity with mainframe tools like IBM z/OS, data migration utilities, and certifications such as IBM Certified System Programmer are typically required. Strong analytical thinking, problem-solving skills, and effective communication are vital soft skills for this role. These abilities ensure reliable data operations, efficient troubleshooting, and successful collaboration within large-scale enterprise environments.

What are some common challenges mainframe data engineers face when integrating legacy systems with modern data platforms?

Mainframe Data Engineers often encounter challenges when integrating legacy mainframe systems with modern data platforms, such as data format incompatibilities, security concerns, and limited documentation of older systems. Addressing these issues typically requires a deep understanding of both mainframe technologies (like COBOL, DB2, or VSAM) and modern data frameworks. Collaboration with cross-functional teams, including application developers and data architects, is essential to ensure seamless data migration and real-time data access, while maintaining data integrity and compliance.

What is the difference between Mainframe Data Engineer vs Data Warehouse Engineer?

AspectMainframe Data EngineerData Warehouse Engineer
CredentialsTypically requires a degree in Computer Science or related field, with knowledge of mainframe technologiesRequires a degree in Computer Science, Data Management, or related field, with expertise in data warehousing tools
Work EnvironmentWorks primarily on mainframe systems, handling legacy data processingWorks on modern data warehouses, integrating data from multiple sources
Industry UsageCommon in banking, finance, and government sectors with legacy systemsUsed across industries for business intelligence and analytics

The Mainframe Data Engineer focuses on maintaining and developing data solutions on legacy mainframe systems, while the Data Warehouse Engineer designs and manages data warehouses for analytics. Both roles require strong data management skills but differ in technology focus and work environment.

What cities are hiring for Mainframe Data Engineer jobs?

Cities with the most Mainframe Data Engineer job openings:

What states have the most Mainframe Data Engineer jobs?

States with the most job openings for Mainframe Data Engineer jobs include:

What are popular job titles related to Mainframe Data Engineer jobs?

For Mainframe Data Engineer jobs, the most frequently searched job titles are:

Infographic showing various Mainframe Data Engineer job openings in the United States as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 83% Full Time, 12% Part Time, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $129,716 per year, or $62.4 per hour.

Test Data Architect

Minneapolis, MN • On-site

Argyllinfotech
See more jobs at Argyllinfotechexternal

$66.50 - $85.50/hr

Contractor

Posted 24 days ago

Job description

Role: Test Data Architect
Location: MN (on-site 3 days)
 
Client: US Bank (Through CTS) 
 
The Test Data Architect is responsible for defining, designing, and governing enterprise test data strategies that support application testing, modernization, automation, and software delivery across complex environments.
 
The role requires strong experience in enterprise data architecture, data modeling, database design, data analysis, test data management, data quality, integration patterns, and SQL. The person will work closely with business, product, architecture, development, testing, and operations teams.
 
Key Responsibilities
 
Understand business processes, data requirements, source systems, and downstream data consumption.
Translate business requirements and rules into logical and physical data models, source-to-target mappings, and integration specifications.
Define enterprise test data strategies supporting functional, regression, integration, performance, automation, and modernization testing.
Design reusable test data patterns including data provisioning, data masking, synthetic data, and environment refresh.
Understand and analyze ER diagrams, schemas, data dictionaries, data flows, entities, attributes, relationships, primary/foreign keys, indexes, and constraints.
Perform data discovery, analysis, profiling, and data mining across databases, files, mainframe systems, and other sources.
Validate data quality, integrity, consistency, reconciliation, transformation logic, and business rules.
Understand enterprise integration patterns including batch processing, real-time/event-driven architecture, APIs, messaging queues, data pipelines, and file-based interfaces.
Support data privacy, protection, retention, classification, and regulatory requirements, particularly for sensitive customer and financial data.
Document data models, mappings, test data patterns, provisioning processes, standards, and reusable best practices.
Collaborate across architecture, engineering, QA, operations, product, and business teams.
Key Skill Set
 
Core Data Skills: Data Architecture • Data Modeling • Logical & Physical Data Models • Database Design • ERD Analysis • Source-to-Target Mapping • Data Lineage • Data Discovery • Data Profiling • Data Quality • Data Reconciliation
 
Test Data: Test Data Management • Test Data Strategy • Data Provisioning • Data Masking • Synthetic Data • Environment Refresh • Functional Testing • Regression Testing • Integration Testing • Performance Testing • Test Automation
 
Integration: APIs • Batch Processing • Event-Driven Architecture • Messaging Queues • Data Pipelines • File-Based Interfaces • Downstream Data Consumers
 
Databases: DB2 • SQL Server • Oracle • PostgreSQL
 
Query / Mainframe: SQL • Easytrieve • SPUFI • COBOL (basic understanding) • JCL (basic understanding) • VSAM datasets • Mainframe data structures • Batch jobs
 
Governance & Security: Data Privacy • Data Protection • Data Retention • Data Classification • Regulatory Compliance • Sensitive Customer and Financial Data
 
Business / Leadership: Business Requirements • Data Requirements • Stakeholder Management • Cross-functional Collaboration • Agile Delivery • Translating business rules into technical data requirements • Communicating complex data concepts to technical and non-technical audiences
 
Most Important Skills for the Interview:
I would prioritize Data Architecture + Data Modeling + SQL + Test Data Management + Mainframe Data + Data Quality/Reconciliation + Integration Patterns.
 
The posting specifically says the interviews will be highly technical, so I would expect scenario-based questions such as designing test data for a complex banking system, tracing data from DB2/mainframe through APIs into downstream systems, explaining logical vs. physical models, writing SQL for reconciliation, designing masking/synthetic-data approaches, and troubleshooting data mismatches across systems.