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Data Engineer

Espoo
€680 - €760 per day

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

Data Engineer

Espoo, Finland

90 EUR Per Hour

4 Months

As a Data Engineer you will be developing automation for cloud-based data pipelines to aid this growing tech company to provide a streamlined customer retail experience. You will be deploying both existing and new data pipelines to GCP.

THE COMPANY:

The client is a growing tech company whose mission is to utilise data and technology to improve the retail experience for the consumer. As a Data Engineer, you will focus on automating data pipelines deployed to GCP which have previously been deployed manually.

THE ROLE:

As a Data Engineer you will be expected to be able to create cloud-based data pipelines. You will also need to be able to redeploy existing pipelines using the best data engineering practices. It is essential to have experience with GCP, where you will be automating the deployment of data pipelines.

YOUR SKILLS AND EXPERIENCE:

The ideal Data Engineer will have:

  • Commercial experience in managing cloud infrastructure as code for GCP
  • Proficiency in Python and SQL
  • Expertise in making custom Google functions
  • Commercial experience in setting up data pipelines in Google Data Fusion
  • Commercial experience using Google BigQuery

HOW TO APPLY:

Please submit your CV to Lily Desmond at Harnham via the Apply Now button.

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45345 - LD
Espoo
€680 - €760 per day
  1. Contract
  2. Data Architecture

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Using Data Ethically To Guide Digital Transformation

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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. 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