Implify Inc
Datastage Analyst/developer USC GC GC EAD
Plano, TX, United States · Mid · Full-time
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About the role
- Implify, Inc is a Global IT Solutions and services firm.
- Since it's inception, Implify, Inc has been providing best-quality and cost-effective IT solutions to fortune 1000 companies, mid-range companies and upcoming companies via its onsite, Offshore and in-house service models.
- IMPLIFY is an IT consulting services and software development firm dedicated to business success through long-term relationships with our clients and staff.
Requirements
- At least 2 years of experience in Information Technology
- 2+ years of experience in software development life cycle
- 2+ years of experience in DW/BI related technologies and tools.
- 2+ years of experience in Project life cycle activities on DW/BI development and maintenance projects
- At least 2 years of experience in Design and Architecture review
- Knowledge of data loading techniques using Datastage
- Knowledge Best Practices in Datastage, Performance improvements, implementing CDC, UDFs
- Strong understanding of scheduling tools such as Autosys, Control- M, Tivoli etc.
- Provides expertise in determining the strategic technical direction and system architecture for initiatives.
- Extensive Experience in Oracle SQL, PL/SQL.
Nice to have
- Bachelor's degree or foreign equivalent required from an accredited institution.
- At least 4 years of experience with Information Technology
- technologies and tools.
- life cycle activities on DW/BI development and maintenance projects
- Architecture review
- using different stages like Aggregator, Join, Merge, Lookup, Source dataset, External Filter, Row generator, Column Generator, Change Capture, Copy, Funnel, Short, Peek stages etc
- monitoring the jobs.
- Performance improvements, implementing CDC, UDFs
Equal opportunity
- All your information will be kept confidential according to EEO guidelines.
Visa & Work Authorization
- Visa Status:
This listing is sourced directly from Implify Inc's careers page and normalized into a canonical job model.