Join us and shape the future of travel
Shaping the future of travel has always been important to us at Amadeus. Today, with technology getting smarter by the minute, that future is more exciting than ever.
We work at the heart of the global travel industry. Amadeus offers you the opportunity to learn and grow and drive your own progression in an exciting and multicultural environment.
Our people are driven by a passion for 'Where next?' If you want to shape your career and the future of travel, Amadeus is the place for you.
The Data Distribution Frameworks team is a newly created team which inherited from challenging topics as Amadeus is more and more embracing the Cloud.
We develop middleware products which are then used by numerous Amadeus application to build their own software.
We are responsible for handling the distribution of the middleware data used by all the Amadeus applications.
This data has to be made available both inside the existing Amadeus Data Center, and also to remote DCs.
We also own the Easy Tables framework, a key-value oriented framework widely used in Amadeus to store Amadeus applications’ configuration and applicative data.
Our team, as a middleware team, is involved in almost all Amadeus software; that means software quality is a must and on top of that response time of our libraries has to be as small as possible.
We are currently missing code benchmarking tools and we have recently migrated from C++03 to C++11 and soon to C++14.
So far, all code has been smoothly migrated to C++11 so that the minimal requirements are met: code builds and we have no process coring during its execution time.
First step will consist in identifying a set of relevant functional use cases, isolate them for local reproduction and build a tool to benchmark them.
Once done, this tool will be run to benchmark and compare our codebase compiled according to various C++ standards (C++03, C++11, C++14) and deliver a report in order to define a hierarchy in the optimizations to be done. This will represent the project state of art.
Then, based on the aforementioned benchmarking results on C++11/14 code base, and according to an initial internal document containing optimization guidelines (to be enriched by the trainee), code will have to be optimized using native C++11/14 features.
There will be a regular meeting status during when we’ll discuss the current optimized code benchmark results and define the next optimization to do.
This is a good opportunity to learn a lot on the middleware components we use in Amadeus as well as functional use cases using it, and to learn C++ code optimization techniques and benchmarking.
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