Job Description :
Position- Sr. Ab Initio ETL Engineer
Location- Washington, DC
Duration - 12+ Months

Tasks:
1. Provide day-to-day maintenance and development support for the FEP Operations Center’s Ab Initio ETL processes for the error handling for the Section 111 Reporting project
2. Generate and maintain system documentation for ETL/DI processes developed
3. Assist with troubleshooting of system and application performance related issues and recommend fixes.
4. Other duties as assigned.

Requirements:
1. 5+ years of experience of hands-on work designing and implementing data integration solutions for data warehouse and business intelligence systems
2. 5+ years using, configuring, and tuning industry leading ETL software tools in data intensive, large scale environments.
3. 5+ years hands-on design, development and support experience with the Ab Initio product suite using GDE, Co>Operating System and EME. Experience in any of the following is desired: Query>It, Conduct>It, Express>It, Continuous flows, Metadata Hub.
4. 3+ years hands-on experience working in Unix/Linux environment, with proficiency in Unix shell scripting (Korn, Bourne, Awk, Sed, etc
5. 3+ years hands-on experience with scheduling tools, preferably, Control-M
6. Strong SQL.
7. Strong ability to effectively communicate (oral presentation) concrete concepts to a variety of stakeholders across business and information technology organizations in a formal and informal manner.
8. Strong writing skills including attention to metadata and definitional detail as well as ability to visually depict concepts to technical and non-technical audiences.
9. Proven ability to work close with others, to work through design challenges, and to solve problems.
10. Proven ability to work both independently and in groups as a self-motivated and highly engaged contributor.
11. 1+ years of experience working with Health Insurance (Payer) organizations or a highly related field including knowledge of Health Level-7 (HL7) and / or related Claims, Enrollment, and Benefits Administration industry data models / taxonomies is preferred (not required
             

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