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Data Engineering and Big Data jobs

What We Do

Processing Big Data has become crucial to countless businesses. Those who decide to pursue a career within Big Data face complex development challenges, too tough for relational database systems. After all, there is no Data Science without Data Engineers.

Whilst businesses may have vast quantities of data at the ready, it holds no real value unless it can be stored, harnessed and utilised. It’s estimated that most UK companies have at least 100 terabytes of data stored which could be used for various purposes around the business, as well as newly accessible hard-to-process sources such as web data, image data, and social media data. 

This is where those who work in Data Engineering become extremely valuable, as they develop Data Platforms that allow this kind of volume to be processed and used by Data Scientists and analysts across the business.

Whether you are a company looking to build a data platform for advanced analytics, or you are a candidate who can build systems and applications to process vast, complex data sources, Harnham are here to help. 

Technical skills: AWS, Google Cloud Platform and Azure, Spark, Hadoop, Java, Scala, Python

Meet the team

Latest Jobs

Business Intelligence Engineer
Salary

£60000 - £75000 per annum

Location

London

Description

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

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Data Engineer
Salary

550000kr - 660000kr per annum

Location

Stockholm

Description

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!

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Senior Data Engineer - Prior Security/Compliance Environment
Salary

US$140000 - US$150000 per year + Additional Benefits

Location

New York

Description

Have this urgent to hire exciting Health-tech opportunity as a Hands-On Senior Data Engineer in NYC! Please view the link below and apply if interested :)

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Data Engineer - Fintech
Salary

€62000 - €72000 per annum + Performance Bonus

Location

Berlin

Description

Would the opportunity to work as a Data Engineer in a leading Fin-tech organisation based in Berlin interest you?

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Data Engineer
Salary

£45000 - £55000 per annum + bonus

Location

London

Description

An exciting opportunity for data engineers to work for the global leader in music licensing in a friendly team who are committed to your technical development

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Big Data Developer
Salary

€55000 - €60000 per annum

Location

Barcelona

Description

Big Data Developer with Spark + Scala and AWS required to work within the automotive industry in several ambitious projects

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Harnham blog & news

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.

Why it is hard to build a Big Data team

Why it is hard to build a Big Data team

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: info@harnham.com

Ross Whatling

Ross Whatling

06. June 2017

How Big Data Is Impacting Logistics

How Big Data is Impacting Logistics

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.

Guest Blog

Guest Blog

29. August 2019

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