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DEVOPS ENGINEER JUNIOR

Paris, Île-de-France
€40000 - €45000 per annum

This vacancy has now expired. Please see similar roles below...

DevOps Engineer Junior
40K-45K par an
Paris, France

Cette Start-Up a développé un logiciel de santé révolutionnant le domaine de la médecine du travail et des professionnels de Santé.

Aujourd'hui elle recherche un DevOps Engineer afin d'accompagner les équipes production et engineering sur toute la partie virtualisation et conteneurisation, monitoring..

Vous jouerez un rôle central dans au sein d'une équipe DevOps en travaillant étroitement avec des profils Data afin d'améliorer le produit en place

LE POSTE :

Au sein de l'équipe Engineering/DevOps :

  • Vous travaillerez sur la partie orchestration/conteneurisation (Docker, Kubernetes, Helm…)
  • Vous travaillerez la roadmap au niveau infra en collaboration avec les équipes product
  • Vous participerez aux différents projets de développement des équipes Data
  • Vous participerez à la migration des applications sur Azure Cloud
  • Vous rédigerez des miscroservices, travaillerez sur la scalabilité
  • Vous prendrez de la hauteur et du recul sur les sujets engineering/infra

LE PROFIL:

  • Diplômé d'un Master Ingénieur ou Scientifique
  • Maitrise de Kubernetes, Docker, ELM
  • Vous connaissez le suite ELK
  • vous avez une solide expérience en production Docker dans un contexte de production et de haute disponibilité, de préférence avec Kubernetes
  • Vous connaissez les langages et outils de gestion de configuration : Ansible, Terraform

COMMENT POSTULER :

Si vous êtes intéressé(e), merci de faire part de votre CV à Pierre Gerbeau

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96714PG
Paris, Île-de-France
€40000 - €45000 per annum
  1. Permanent
  2. Data Architecture

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With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.

Visit our Blogs & News portal or check out our recent posts below.

Using Data Ethically To Guide Digital Transformation

Over the past few years, the uptick in the number of companies putting more budget behind digital transformation has been significant. However, since the start of 2020 and the outbreak of the coronavirus pandemic, this number has accelerated on an unprecedented scale. Companies have been forced to re-evaluate  their systems and services to make them more efficient, effective and financially viable in order to stay competitive in this time of crisis. These changes help to support internal operational agility and learn about customers' needs and wants to create a much more personalised customer experience.  However, despite the vast amount of good these systems can do for companies' offerings, a lot of them, such as AI and machine learning, are inherently data driven. Therefore, these systems run a high risk of breaching ethical conducts, such as privacy and security leaks or serious issues with bias, if not created, developed and managed properly.  So, what can businesses do to ensure their digital transformation efforts are implemented in the most ethical way possible? Implement ways to reduce bias From Twitter opting to show a white person in a photo instead of a black person, soap dispensers not recognising black hands and women being perpetually rejected for financial loans; digital transformation tools, such as AI, have proven over the years to be inherently biased.  Of course, a computer cannot be decisive about gender or race, this problem of inequality from computer algorithms stems from the humans behind the screen. Despite the advancements made with Diversity and Inclusion efforts across all industries, Data & Analytics is still a predominantly white and male industry. Only 22 per cent of AI specialists are women, and an even lower number represent the BAME communities. Within Google, the world’s largest technology organisation, only 2.5 per cent of its employees are black, and a similar story can be seen at Facebook and Microsoft, where only 4 per cent of employees are black.  So, where our systems are being run by a group of people who are not representative of our diverse society, it should come as no surprise that our machines and algorithms are not representative either.  For businesses looking to implement AI and machine learning into their digital transformation moving forward, it is important you do so in a way that is truly reflective of a fair society. This can be achieved by encouraging a more diverse hiring process when looking for developers of AI systems, implementing fairness tests and always keeping your end user in mind, considering how the workings of your system may affect them.  Transparency Capturing Data is crucial for businesses when they are looking to implement or update digital transformation tools. Not only can this data show them the best ways to service customers’ needs and wants, but it can also show them where there are potential holes and issues in their current business models.  However, due to many mismanagements in past cases, such as Cambridge Analytica, customers have become increasingly worried about sharing their data with businesses in fear of personal data, such as credit card details or home addresses, being leaked. In 2018, Europe devised a new law known as the General Data Protection Regulation, or GDPR, to help minimise the risk of data breaches. Nevertheless, this still hasn’t stopped all businesses from collecting or sharing data illegally, which in turn, has damaged the trustworthiness of even the most law-abiding businesses who need to collect relevant consumer data.  Transparency is key to successful data collection for digital transformation. Your priority should be to always think about the end user and the impact poorly managed data may have on them. Explain methods for data collection clearly, ensure you can provide a clear end-to-end map of how their data is being used and always follow the law in order to keep your consumers, current and potential, safe from harm.  Make sure there is a process for accountability  Digital tools are usually brought in to replace a human being with qualifications and a wealth of experience. If this human being were to make a mistake in their line of work, then they would be held accountable and appropriate action would be taken. This process would then restore trust between business and consumer and things would carry on as usual.  But what happens if a machine makes an error, who is accountable?  Unfortunately, it has been the case that businesses choose to implement digital transformation tools in order to avoid corporate responsibility. This attitude will only cause, potentially lethal, harm to a business's reputation.  If you choose to implement digital tools, ensure you have a valid process for accountability which creates trust between yourself and your consumers and is representative of and fair to every group in society you’re potentially addressing.  Businesses must be aware of the potential ethical risks that come with badly managed digital transformation and the effects this may have on their brands reputation. Before implementing any technology, ensure you can, and will, do so in a transparent, trustworthy, fair, representative and law-abiding way.  If you’re in the world of Data & Analytics and looking to take a step up or find the next member of your team, we can help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more.

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It Takes Two: Data Architect Meets Data Engineer

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