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Solutions Engineer - SQL, ETL - Remote Working

London
£45000 - £60000 per annum
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Solutions Engineer - Remote Working

£60,000

London

Do you want to help build a SQL data base for a global company? Do you want to interact with both businesses and clients to get the most out of their data? If so, this could be the role for you!

Company

This role sits within the market share of this IT and Analytics consultancy. They work with clients through several product streams: Audience analytics and intelligence, Programmatic DMP pulling data streams into one platform and the MTA solution pulling media performance analytics into the platform. This role will be working with ever changing and growing data for up to 20 clients at a time. Despite the size of the company, this relatively new London team act like a start up in the business with a high amount of collaboration and constant work with clients.

Role

In this Solution Engineer role, you will:

  • Understand client requirements to configure the business applications, data interactions and data transformations
  • Operate in the full cycle of client implementation, from initial planning to delivery and operation of projects
  • Setup, validate and optimise ETL interactions using SQL, AWS and other tools
  • Communicate with clients and stakeholders to solve issues and identify data challenges

Requirements

To qualify for this Solutions Engineer role, you will require:

  • Advanced SQL skills with the ability to join and filter data sets effectively and efficiently
  • Advanced Excel skills
  • Advanced understanding and experience of ETL best practices and methodologies
  • Experience and understanding of database design
  • Familiarity with different data systems is beneficial

What you'll get

If successful, you will receive:

  • A salary of up to £60,000 depending on experience
  • Regular bonus of up to 10% depending on personal and company performance

HOW TO APPLY:

Please register your interest to this Cloud Data Engineer role by applying via this website. For more information on this role or other roles in the Business Intelligence market, reach out to Tom Brammer at Harnham.

Please note that our client is currently running a fully remote interview process, and able to on-board and hire remotely as well.

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81301
London
£45000 - £60000 per annum
  1. Permanent
  2. Business Intelligence

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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.

2020: The Year of the Data Engineer

Data Engineers are the architects of Data. They lay the foundation businesses use to collect, gather, store, and make Data usable. Each iteration of the Data as it moves along the pipeline is cleaned and analysed to be used by Data professionals for their reports and Machine Learning models. A ROLE IN HIGH DEMAND Even as businesses reopen, reassess, and for some, remain remote, the demand for Data Engineers is high. Computer applications, Data modelling, prediction modelling, Machine Learning, and more need Data professionals to lay the groundwork to help businesses benefit in today’s Data-driven culture. The word gets thrown around a bit, but when the majority of business has moved online, Data-driven is the name of the game. Having a Data plan, a Data team, and all aligned with your business strategy is imperative to the way business is done today. This type of innovation can offer insight for better business decisions, enhance customer engagement, and improve customer retention without missing a beat.  Without Data Engineers, Data Scientists can’t do their jobs. Understanding the amount of Data, the speed at which is delivered, and its variety need Engineers to create reliable and efficient systems. Like many Data professional jobs, even still in 2020, Data Engineers are in high demand. Yet a skills shortage remains. This has created an emerging field of professionals from other backgrounds who are looking to take on the role of Data Engineer and fill the gap. Whether by necessity or design, these individuals build and manage pipelines, automate projects, and see their projects through to the end result. CAREER OPPORTUNITIES OUTSIDE THE NORM As this growing trend emerges, it has created career opportunities for those with experience outside the normal channels of Data Engineering study. While it might involve individuals from backgrounds such as software Engineering, Databases, or something similarly IT-related, some businesses are upskilling their employees with talent. Rapid growth, reskilling, upskilling, and ever-constant changes still leave businesses with a shortage of Data Engineers to meet the demand. It’s critical to fill the gap for success. According to LinkedIn’s 2020 Emerging Jobs Report, Data Engineering is listed in the top 10 of jobs experiencing growth. THREE STEPS TOWARDS BECOMING A DATA ENGINEER This is a vital role in today’s organisations. So, if you’re in the tech industry and want to take a deeper dive into Data as a Data Engineer, what steps can you take? This is a time like no other. There’s time to assess your goals, take online classes, and get hands on with projects. Though having a base of computer science, mathematics, or business-related degree is always a good start. Be well-versed in such popular programming languages such as SQL, Python, R, Hadoop, Spark, and Amazon Web Services (AWS).Prepare for an entry-level role once you have your bachelor’s degree.Consider additional education to stay ahead of the curve. This can include not only professional certifications, but higher education degrees as well. The more experience, hands-on as well as academic, you have the more in demand you’ll be as a Data Engineer. Data scientists might be the rockstars of Data, but Data Engineers set the stage. As business processes have shifted online, looking for your next job has become more daunting than ever before. If you’re looking for your next opportunity in Data, take a look at our current jobs or get in touch with one of our expert consultants to find out more. 

Joshua Carter

Joshua Carter

25. June 2020

News

The Search For Toilet Paper: A Q&A With The Data Society

We recently spoke Nisha Iyer, Head of Data Science, and Nupur Neti, a Data Scientist from Data Society.  Founded in 2014, Data Society consult and offer tailored Data Science training for businesses and organisations across the US. With an adaptable back-end model, they create training programs that are not only tailored when it comes to content, but also incorporate a company’s own Data to create real-life situations to work with.  However, recently they’ve been looking into another area: toilet paper.  Following mass, ill-informed, stock-piling as countries began to go into lockdown, toilet paper became one of a number of items that were suddenly unavailable. And, with a global pandemic declared, Data Society were one of a number of Data Science organisations who were looking to help anyway they could.  “When this Pandemic hit, we began thinking how could we help?” says Iyer. “There’s a lot of ways Data Scientists could get involved with this but our first thought was about how people were freaking out about toilet paper. That was the base of how we started, as kind of a joke. But then we realised we already had an app in place that could help.” The app in question began life as a project for the World Central Kitchen (WCK), a non-profit who help support communities after natural disasters occur.  With the need to go out and get nutritionally viable supplies upon arriving at a new location, WCK teams needed to know which local grocery stores had the most stock available.  “We were working with World Central Kitchen as a side project. What we built was an app that supposed to help locate resources during disasters. So we already had the base done.” The app in question allows the user to select their location and the products they are after. It then provides information on where you can get each item, and what their nutritional values are, with the aim of improving turnaround time for volunteers.  One of the original Data Scientists, Nupur Neti, explained how they built the platform: “We used a combination of R and Python to build the back-end processing and R Shiny to build the web application. We also included Google APIs that took your location and could find the closest store to you. Then, once you have the product and the sizes, we had an internal ranking algorithm which could rank the products selected based on optimisation, originally were based on nutritional value.”  The team figured that the same technology could help in the current situation, ranking based on stock levels rather than nutritional value. With an updated app, Iyer notes “People won’t have to go miles and stand in lines where they are not socially distancing. They’ll know to visit a local grocery store that does have what they need in stock, that they’ve probably not even thought of before.” However, creating an updated version presented its own challenges. Whereas the WCK app utilised static Data, this version has to rely on real-time Data. Unfortunately this isn’t as easy to come by, as Iyer knows too well:  “When we were building this for the nutrition app we reached out to groceries stores and got some responses for static Data. Now, we know there is real-time Data on stock levels because they’re scanning products in and out. Where is that inventory though? We don’t know.” After putting an article out asking for help finding live Data, crowdsourcing app OurStreets got in touch. They, like Data Society, were looking to help people find groceries in short supply. But, with a robust front and back-end in place, the app already live, and submissions flying in across the States, they were looking for a Data Science team who could make something of their findings.  “We have the opportunity,” says Iyer “to take the conceptual ideas behind our app and work with OurStreets robust framework to create a tool that could be used nationwide.” Before visiting a store, app users select what they are looking for. This allows them to check off what the store has against their expectations, as well as uploading a picture of what is available. They can also report on whether the store is effectively practising social distancing. Neti explains, that this Data holds lots of possibilities for their Data Science team: “Once we take their Data, our system will clean any submitted text using NLP and utilise image recognition on submitted pictures using Deep Learning. This quality Data, paired with the Social Distancing information, will allow us to gain better insights into how and what people are shopping for. We’ll then be able to look at trends, see what people are shopping for and where. Ultimately, it will also allow us to make recommendations as to where people should then go if they are looking for a product.”  In addition to crowdsourced information, Data Society are still keen to get their hands on any real-time Data that supermarkets have to offer. If you know where they could get their hands on it, you can get in touch with their team.  Outside of their current projects, Iyer remains optimistic for the world when it emerges from the current situation: “Things will return to normal. As dark a time as this is, I think it’s going to exemplify why people need to use Artificial Intelligence and Data Science more. If this type of app is publicised during the Coronavirus, maybe more people will understand the power of what Data and Data Science can do and more companies that are slow adaptors will see this and see how it could be helpful to their industry.”   If you want to make the world a better place using Data, we may have a role for you, including a number of remote opportunities. Or, if you’re looking to expand and build out your team with the best minds in Data, get in touch with one of expert consultants who will be able to advise on the best remote and long-term processes. 

Nick Mandella

Nick Mandella

14. April 2020

News

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