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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

Data Engineer
Salary

£55000 - £70000 per annum

Location

London

Description

New role just in - Data Engineer for a growing e-commerce business based in Central London.

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Cloud Engineer
Salary

£60000 - £65000 per annum

Location

London

Description

Join a leading fin-tech as a Cloud Engineer where you will be responsible for designing, building and managing automated software deployment solutions.

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Technical Architect
Salary

£60000 - £80000 per annum

Location

London

Description

Exciting new technical architect role where you will have full autonomy across multiple projects and products!

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Lead Data Engineer - Fortune 500
Salary

US$440 - US$520 per day + Additional Benefits

Location

New York

Description

Hi All, I'm currently recruiting for this position. Please click on the job title below to view the Job Description and apply to it!

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Data Engineer
Salary

£40000 - £60000 per annum + + Bonus + Competitive Benefits

Location

London

Description

Are you looking to join one of the largest retailers, with one of the most visited websites in the UK as a Data Engineer?

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Staff Data Engineer
Salary

US$243850 - US$268235 per annum + Benefits

Location

New York

Description

A fast-growing tech company is looking for a Staff Data Engineer to join them in the heart of New York City! The salary is up to $220k, click below to apply!

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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.

What Does The Fourth Industrial Revolution Look Like?

We’re in the next stage of the fourth Industrial Revolution and technologies continue to merge. No longer is advancement as simple as adding “tech” to the end of a word - sorry Fintech, InsurTech, HRTech, and the rest. Now technologies stand together as each becomes a separate piece of how tech operates in the business world. AI and IoT have merged to become AIoT. Data is as much commodity as it is information to fuel business growth. Computer Vision partnered with AI is teaching computers to convert their ones and zeros to images humans take for granted.   In a word, it’s a transformative time for every industry and every industry is taking advantage of the benefits in one way or another. Smart manufacturing. Human Resources. Marketing. Even insurance has joined the party. But, with so many advancements, we thought we’d take a look at just the tip of the iceberg, starting with A, B, and C.  AI Meets IoT  We’ve all heard how AI and the Internet of Things (wearable and smart devices etc.) are being used in the Health sector. With the kind of real-time Data available, patients, insurers, and medical professionals can map out health plans based on wearable devices to track patient health and encourage preventative care.  Indeed, one insurance company is embracing these Data trends to ramp up the speed and efficiency of their data. Using Machine Learning and IoT sensors to develop an AI-based solution, customer information is used to match clients with the right policies tailored to their needs.  Car insurance is another industry to benefit. Insurers are able to collect real-time driving data which they can analyse to determine risk or offer discounted policies for good driving. This kind of information can also be used to revisit and reconstruct accident scenes to figure out what happened and who’s at fault.  Big Data, Big Money We’ve all heard the phrase ‘Data Is The New Oil’ by now, which I’m sure we can all agree, just means Data is a resource everybody wants and is willing to pay a lot for. But the differences between Data and Oil are two-fold; Data has the potential to be infinite, and it tells us about what oil cannot; the human experience.  Cloud technologies, edge hardware, and the IoT have helped shape the digitisation of objects, people, and organisations. From sensors to wearable devices, more and more data is being collected, allowing us to be more connected than ever before. It’s also providing more information to the tech giants than ever before. For example, Amazon’s Ring doorbell is logging every motion around it and can pinpoint the time to millisecond.   Add these technologies to Natural Language Processing (NLP) and watch the world around us draw value from and understand our Data like never before. The wave of Big Data value shows no signs of slowing down. Computer Vision in Business In the last few years, Computer Vision has been making great strides in the business world. Yet the Data required for processing power and memory can still be impacted by image quality. The opportunities  are alive with possibility and, from small businesses to enterprise solutions, Computer Vision has seen a variety of industries finding practical business uses.  Below are just a few additional areas Computer Vision is making its mark. Facial Recognition – providing surveillance and security systems in such areas as police work, payment portals, and retail stores.Digital Marketing – sorting and analysing online images to target ad campaigns to the right audiences.Financial Institutions –preventing fraud, allowing mobile deposits, analysing handwriting, and beyond. With the global market for fourth industry technologies predicted to be between $17.4 billion and $48.32 billion by 2023, now is the time to find your focus within the industry.  Ready to take the next step in your career? Whether you’re interested in AI, Big Data and Analytics, Computer Vision or more, we may have a role for you. Take a look at our current opportunities or get in touch with one of our expert consultants to find out more.  

Krishen Patel

Krishen Patel

05. March 2020

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

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