Temporary, Contractor, Full-time
Analysis Skills, Application Programming Interface (API), Best Practices, Business Plan, Cloud Computing, Cobol Programming Language, Communication Skills, Cross-Functional, Data Analysis, Data Mapping, Data Migration, Data Modeling, Data Profiling, Data Quality, Data Structures, Database Extract Transform and Load (ETL), Database Technology, IBM DB2, IBM IMS Database, IBM Mainframe, IBM z-OS Operating System, IP Multimedia System (IMS), Informatica, JCL (Job Control Language), Mainframe Computer, Mentoring, Microsoft SQL Server, Microsoft Transact-SQL (T-SQL), Performance Tuning/Optimization, Problem Solving Skills, Reconciliation, SQL (Structured Query Language), SQL Server Integration Services (SSIS), Salesforce.com, Scripting (Scripting Languages), Stored Procedures, Team Lead/Manager
Title: Mainframe Data Conversion Lead
Duration: Contract
Location: Hybrid- (once a week in Office in Phoenix, AZ)- (Local Arizona Candidate only)
Rate: Upto $69/hr on W2 or $76/hr on C2C or 1099 (All inclusive / no benefits)
Job Description:
Required Skills
Previous experience in Mainframe data conversion (This must be on Resume)
Salesforce Expert
Informatica Expert
SQL Server Expert
Preferred Skills
Informatica Data Quality (IDQ)
MuleSoft
Salesforce Service Cloud
This is a Hybrid position and expected to be in office once a week. Local candidates only.
We are seeking an experienced Mainframe Data Conversion Lead to lead the complex migration of legacy mainframe data (primarily IBM z/OS, DB2, VSAM, IMS, and COBOL-based systems) into Salesforce using Informatica as the primary ETL tool and SQL Server as the staging and transformation platform.
The ideal candidate will have deep expertise in mainframe data structures, data mapping, cleansing, transformation, and loading into Salesforce (Service Cloud, and custom objects). This role is critical in ensuring data accuracy, integrity, and compliance during large-scale data conversion initiatives.
Key Responsibilities
- Lead end-to-end mainframe data conversion projects from discovery through go-live and hypercare.
- Analyze and document mainframe data structures including DB2 tables, VSAM files, IMS databases, flat files, and COBOL copybooks.
- Design and develop data mapping documents, source-to-target mappings, and transformation rules from mainframe sources to Salesforce objects.
- Build, optimize, and maintain robust ETL processes using Informatica PowerCenter / IICS for extraction, cleansing, transformation, and loading.
- Use SQL Server (stored procedures, SSIS, SQL scripts) as an intermediate staging, cleansing, and reconciliation platform.
- Work closely with Salesforce architects and developers to ensure data is loaded efficiently using Salesforce APIs, Bulk API, and Data Loader (or Informatica Salesforce Connector).
- Perform data profiling, data quality assessment, cleansing, deduplication, and enrichment.
- Develop and execute data validation, reconciliation, and cutover plans with business and technical teams.
- Mentor junior team members.
- Establish and enforce data conversion best practices, standards, and governance.
- Provide regular status reporting to project management and stakeholders on data conversion progress, risks, and issues.
Qualifications
- 10+ years of overall data integration / ETL experience with at least 5+ years specifically in Mainframe to Modern Platform data migrations.
- Strong experience migrating data from IBM Mainframe environments (z/OS, DB2, VSAM, IMS, COBOL, JCL).
- Expert-level hands-on experience with Informatica Intelligent Cloud Services (IICS).
- Strong proficiency in SQL Server development (T-SQL, stored procedures, performance tuning).
- Solid experience with Salesforce data migration (Salesforce objects, Data Loader, Bulk API, Salesforce Connect, External Objects).
- Deep understanding of data modeling, normalization, hierarchical vs relational data structures, and mainframe-to-relational conversion techniques.
- Experience in large-scale Data Conversion / Legacy Modernization projects (highly preferred).
- Strong knowledge of data quality frameworks and tools.
- Excellent analytical, problem-solving, and communication skills.
- Ability to work independently and lead cross-functional teams.