Data Architect

Job Description

Job Description

TECHNICAL SKILLS
Must Have:
COBOL, CICS, DB2, DB2 utilities, SQL JCL, MVS Utilities, Experience doing Production Support Use of CA7 Scheduling tool. 
Need to be able to do analysis on the as-is and come up with design of the to be, Use of Jira

 
Nice To Have
Banking Domain Knowledge, Strong experience in data analysis, data architecture, database design, test data management, quality engineering, or software delivery. Proficiency writing and optimizing SQL queries for data discovery, validation, reconciliation, and troubleshooting. Experience analyzing relational databases, mainframe data structures, files, batch jobs, APIs, and downstream data consumers.

 
LOCATION: Minneapolis, MN 55343

 
Key Responsibilities

• Partner with business stakeholders, Product Owners, SMEs, analysts, and development teams to understand business processes, data requirements, source systems, data mappings, and data consumption needs.
• Translate business requirements, processes, and rules into logical and physical data models, source-to-target mappings, and data integration specifications.
• Define and implement test data strategies that support functional, regression, integration, performance, automation, and modernization testing needs.
• Design and govern reusable test data patterns, test data provisioning approaches, masking strategies, synthetic data solutions, and environment refresh processes.
• Demonstrate strong understanding of data modeling principles, including entities, attributes, relationships, schemas, primary and foreign keys, indexes, constraints, and overall database design.
• Read, interpret, and analyze Entity Relationship Diagrams, database schemas, data dictionaries, and end-to-end data flow diagrams to identify data dependencies and business impact.
• Perform data discovery, analysis, and data mining across multiple databases, tables, files, and source systems using SQL, Easytrieve, and other data query and extraction tools.
• Validate data quality, integrity, consistency, and accuracy by analyzing data relationships, transformation logic, reconciliation results, and business rules across systems.
• Develop an understanding of data movement and integration patterns, including real-time and batch processing, event-driven architectures, APIs, messaging queues, data pipelines, and file- based interfaces.
• Collaborate with architecture, development, testing, and operational teams to support data analysis, problem resolution, impact assessments, test automation enablement, and system modernization initiatives. 
• Ensure test data solutions comply with data privacy, data protection, retention, classification, and regulatory requirements for sensitive customer and financial data.
• Document data models, test data design patterns, source-to-target mappings, provisioning processes, standards, and best practices for repeatable enterprise use.

• Bachelor's degree in Computer Science, Information Technology, Data Management, Engineering, or a related field, or equivalent practical experience.
• Strong experience in data analysis, data architecture, database design, test data management, quality engineering, or software delivery. 
• Proficiency writing and optimizing SQL queries for data discovery, validation, reconciliation, and troubleshooting. 
• Experience analyzing relational databases, mainframe data structures, files, batch jobs, APIs, and downstream data consumers. 
• Ability to translate business rules and system behavior into clear data requirements, mappings, and test data needs. 
• Strong communication skills with the ability to explain complex data concepts to technical and non-technical stakeholders. 
• Experience working in Agile delivery environments and collaborating across product, engineering, quality, architecture, and operations teams. 
• Preferred Skills & Technologies Databases

• Experience working within banking, payments, credit card, deposits, lending, or customer/account data domains.
• Understanding of core banking platforms, transaction processing flows, customer and account hierarchies, and regulatory reporting requirements.
• Ability to trace, analyze, and map data across upstream and downstream applications, databases, files, APIs, and enterprise data platforms.
• Familiarity with batch and real-time processing architectures and the movement of data across interconnected banking systems.
• Experience with enterprise test data management platforms, data masking, synthetic data generation, data subsetting, and automated data provisioning is preferred.
• Understanding of data governance, data classification, retention requirements, and regulations governing the storage, transmission, and protection of sensitive customer and financial data.

Flexible work from home options available.