Data Engineer

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Primary requirement:-

DataStage
Design, develop, and maintain DataStage ETL jobs according to specifications.
Test DataStage jobs for accuracy and performance
Reviewing job designs and code created by other DataStage developers.
Monitor DataStage job execution and troubleshoot issues as needed.    
Provide support for production DataStage jobs as needed
Follow best practices and maintain documentation for all the developement/modifications
 
 
Microstrategy
Understanding of MSTR projects (Desktop, Web & Intelligent servers
Support and modify applications to meet business process and application requirements.
Hands on experience with desktop, MSTR Web,Command Manager, Integrity Manger and Object manager
Knowledge on best practices on MicroStrategy
Experience in SQL Database To ensure integrity of the application
 

Experience of working in Agile Methodology

Open to learn and work in Azure Data Engineer stack - Azure Data factory, Azure Databricks, Azure datalake store, PowerBI.

KEY EXPECTED ACHIEVEMENTS

Following Lean & Agile methods, quality and security (SecByD) rules, and state of the art of DevOps practices under the supervision of senior and lead engineer:

Software, creating business value, is developed and maintained:
The beautiful* and clean** code is written. Testings and debugging are completed. The softwares are maintained and improved. Technical support by diagnosing and solving simple incidents is insured. Easy technical questions are answered. Minor changes to existing applications are implemented. The associated documentation is carried out.

Functional and/or Technical specifications are adapted to cover the right level of business needs:
A great User Interface (UI)/ User eXperience (UX) is provided by working closely with the business partners or UX/UI experts. Functional and/or Technical specifications are adapted accordingly.

Controls and tests are executed:
Unit tests and unit plans are built, executed, optimized, ensuring quality, security and consistency. Malfunctions, incidents and bugs  are detected, understood, analyzed, reported and solved.

The solution is deployed and operated:
He/she is a contributor to change management, communication and training.
He/she builds an automated deployment.
He/she supports the solution (especially in product mode)

* Beautiful code is showable, useful, generic, sustainable (ease of maintenance,  scalable, reusable), shareable, elegant (code as essay, "bookish", minimal), pleasurable, efficient, concise, readable.
** Clean code uses writing standards (names (search), functions (short), ...), code patterns (architecture/design at all scale) and practices (testing & refactoring) and uses code discipline (SOLID, DRY, YAGNI, TDD, ...).