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BIG DATA ENGINEER (m/f/d)
75.000 € - 95.000 € + BENEFITS
Unser Kunde, ein modernes Beratungshaus im Bereich Big Data und Data Warehousing sucht derzeit nach einem erfahrenen Big Data Engineer. Als Teil eines Teams aus Spezialisten hast du die Möglichkeit an anspannenden Projekten zu arbeiten! Du wirst von äußerst erfahrenen Kollegen lernen und deine Fähigkeiten erweitern.
Als Big Data Engineer erwarten dich folgende Aufgaben:
DEINE SKILLS UND EXPERIENCE
Als erfolgreicher Big Data Engineer solltest du folgende Qualifikationen mitbringen:
Wenn du an dieser Position interessiert bist, bewirb dich direkt auf dem Link oder kontaktiere Marcel Mathis
£60000 - £75000 per annum
Looking for a hands on Business Intelligence Engineer to work with a Top UK Start-up where you will have play a key role in maximising the value of pipelines
€62000 - €72000 per annum + Performance Bonus
Would the opportunity to work as a Data Engineer in a leading Fin-tech organisation based in Berlin interest you?
£70000 - £80000 per annum
This is an exciting opportunity for a talented data engineer using SQL to join a growing company working with the government to fight cryptocurrency crime.
550000kr - 660000kr per annum
Want to work in one of the hottest Fintech start-ups globally? Have at least 2 years of experience as a data engineer? Take a look at the job below!
£550 - £600 per day
Are you looking for an opportunity to work on an AWS Platform alongside Big Data Technologies?
With over 10 years experience working solely in the Data & Analytics sector our consultants are able to offer detailed insights into the industry.
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Increasingly, I speak to managers who are adopting big data tools and developing PoCs to prove how they can make use of them. Just last week I spoke to a data architect who mentioned that if he didn’t get exposure to big data tech sooner rather than later, his current RDBMS skills may become redundant within the next few years. While that is likely an exaggeration, it is certainly an interesting point. Companies that would have never previously had the capability to interpret ‘Big Data’ are now exploring a variety of NoSQL platforms. In particular, the massive performance benefits gained from Spark and real-time/streaming tools have opened up a whole new world beyond just MapReduce. I don’t claim to be a data engineer, but as a recruiter for this sector, what I do is spend all day, every day interacting with big data developers, architects and managers (as well as keeping a close eye on the latest Apache incubator projects). Due to this, I have seen some recurring themes that have become trends when companies look to create and build their big data teams that are coming to the fore. Candidate demand The demand for Big Data professionals is very much a present day issue as the data companies have grand plans for is waiting for the right data developer to use the best tech to extract valuable insights from it. The best candidates receive massive interest, often gain multiple offers from a range of companies. Your business is now no longer just competing with large corporations such as Facebook, Twitter or Yahoo. Startups and SMEs are also vying for the best candidates. Candidates are seeing pay rises twice that of the normal rate, as illustrated in our salary guide. Candidate shortage The number of candidates with hands-on, production level Big Data experience is incredibly limited. We go to great lengths to find the candidates who can add real value to companies. The growth and exciting future for the big data industry has led to increased interest in big data jobs, particularly for those from RDBMS or software. engineering backgrounds. This leaves the industry in a difficult predicament: high demand + low supply = massive competition. There are countless examples of companies that have failed to recruit a Big Data team after a year of looking. Competition to get ahead and stand out Planning - Companies need to have a data road map detailing their future plans. Candidates want to clearly know what they are getting into and what to expect from a job. Innovation - Why get stuck on batch processing? The most exciting positions that candidates love are in data innovations teams, playing with real-time/streaming tech and new languages. Personal development, growth and training – with the data science market experiencing similar growth, many big data engineers are looking for a job that not only offers the chance to work with machine learning and similar fields; but training, mentoring towards clear career progression as standard. Speed – the length of the interview process is often seen as a reflection of the amount of red tape developers have to go through to get a job. The longer and more convoluted the process, the more put off some people may be. Complacency – don’t rest on your laurels, it’s unlikely that you’ll get 10s of CVs through when you are looking to fill a data role, so when you find a candidate you like, move swiftly to show your interest to them as quality candidates don’t come around often. By implementing these small but effective improvements to your recruiting process and how you develop data talent will see you create a team that is a success in this ever more digital analytics landscape. Companies who don’t create and nurture strong, dynamic teams will fall by the wayside. It’s Harnham’s job to help you achieve this goal. Get in touch with us to tell you how. T: (020) 8408 6070 E: firstname.lastname@example.org
06. June 2017
As Big Data can reveal patterns, trends and associations relating to human behaviour and interactions, it’s no surprise that Data & Analytics are changing the way that the supply chain sector operates today. From informing and predicting buying trends to streamlining order processing and logistics, technological innovations are impacting the industry, boosting efficiency and improving supply chain management. Analysing behavioural patterns Using pattern recognition systems, Artificial Intelligence is able to analyse Big Data. During this process, Artificial Intelligence defines and identifies external influences which may affect the process of operations (such as customer purchasing choices) using Machine Learning algorithms. From the Data collected, Artificial Intelligence is able to determine information or characteristics which can inform us of repetitive behaviour or predict statistically probable actions. Consequently, organisation and planning can be undertaken with ease to improve the efficiency of the supply chain. For example, ordering a calculated amount of stock in preparation for a busy season can be made using much more accurate predictions - contributing to less over-stocking and potentially more profit. As a result, analysing behavioural patterns facilitates better management and administration, with a knock-on effect for improving processes. Streamlining operations Using image recognition technology, Artificial Intelligence enables quicker processes that are ideally suited for warehouses and stock control applications. Additionally, transcribing voice to text applications mean stock can be identified and processed quickly to reach its destination, reducing the human resource time required and minimising human error. Artificial intelligence has also changed the way we use our inventory systems. Using natural language interaction, enterprises have the capability to generate reports on sales, meaning businesses can quickly identify stock concerns and replenish accordingly. Intelligence can even communicate these reports, so Data reliably reaches the next person in the supply chain, expanding capabilities for efficient operations to a level that humans physically cannot attain. It’s no surprise that when it comes to warehousing and packaging operations Artificial Intelligence can revolutionise the efficiency of current systems. With image recognition now capable of detecting which brands and logos are visible on cardboard boxes of all sizes, monitoring shelf space is now possible on a real-time basis. In turn, Artificial Intelligence is able to offer short term insights that would have previously been restricted to broad annual time frames for consumers and management alike. Forecasting Many companies manually undertake forecasting predictions using excel spreadsheets that are then subject to communication and data from other departments. Using this method, there’s ample room for human error as forecasting cannot be uniform across all regions in national or global companies. This can create impactful mistakes which have the potential to make predictions increasingly inaccurate. Using intelligent stock management systems, Machine Learning algorithms can predict when stock replenishment will be required in warehouse environments. When combined with trend prediction technology, warehouses will effectively be capable enough to almost run themselves negating the risk of human error and wasted time. Automating the forecasting process decreases cycle time, while providing early warning signals for unexpected issues, leaving businesses better prepared for most eventualities that may not have been spotted by the human eye. Big Data is continuing to transform the world of logistics, and utilising it in the best way possible is essential to meeting customer demands and exercising agile supply chain management. If you’re interested in utilising Artificial Intelligence and Machine Learning to help improve processes, Harnham may be able to help. Take a look at our latest opportunities or get in touch with one of our expert consultants to find out more. Author Bio: Alex Jones is a content creator for Kendon Packaging. Now one of Britain's leading packaging companies, Kendon Packaging has been supporting businesses nationwide since the 1930s.
29. August 2019