Mandatory Skills: Candidates must meet all the requirements below to be considered for the Senior DataStage ETL Developer position:
• Minimum of five (5) years of recent experience developing applications using IBM DataStage ETL.
• Extensive experience with IBM DataStage Designer and Director.
• Experience migrating mainframe VSAM data to relational and/or multidimensional SQL databases.
• Experience with Legacy and Unstructured data.
• Experience working in multiple development environments/systems: IBM Mainframe,Windows Server, UNIX and SQL Server.
• Strong SQL knowledge working with complex queries and stored procedures.
• Proficiency in mainframe applications encompassing JCL, COBOL, VSAM and DB2.
• Excellent interpersonal, communication, presentation, writing, analytical, problemsolving, and information gathering skills along with fundamental techniquetroubleshooting abilities.
• Strong analytical skills demonstrated by the ability to receive and analyze requirementsand propose a suitable solution that adheres to the team’s methodology
• Experience extracting business rules from the existing legacy code.
• Experience with MS Visual Studio and .NET.
• Experience developing applications in IBM VSE mainframe environment
Assumptions Regarding Consultant Tasks and Deliverables
The ETL Developer shall provide NYCERS’ Data Management with technical expertise forthe completion of Data Quality, Profiling, Cleansing and Migration efforts as well asNYCERS’ ongoing process improvement programs. The following deliverables shall apply:
Analyze business requirements and design applications.
Work with data SME’s to modify applications improving the existing business processes.
Actively participate in optimization and performance tuning of existing ETL and SQLprocesses.
Transform technical specifications into ETL processes to load the data for cleansing prior tothe migration.
Provide business units with ad-hoc deep-dive analytical reports in support of business rulesparsing effort.
Collaborate with technical leads in identifying and addressing production issues and dataquality gaps.
Support processes that proactively detect, correct, and prevent invalid data values.