Top 10 jobs in data - Viewpoint - careers advice blog Viewpoint careers advice blog

Top 10 jobs in data - Viewpoint - careers advice blog Data, as a topic, has hit the big time. From residing in the basement in early 2000, data (and more specifically big data) has risen to the boardroom. A 2014 Gartner survey even predicted that 73 per cent of organisations would be ‘investing heavily’ in big data projects by 2016. With 2016 nearing its end, I think now is a good time to review the data landscape and assess the top ten jobs in data. 10. Chief Data Officer (CDO) It all starts at the top and for those companies serious about unleashing the potential of their data footprint, appointing a CDO is an essential first step. From 400 CDO’s in 2014 to over 1000 in 2015, Gartner suggests that 90 per cent of the UK’s large companies will have a chief data officer by 2019. The CDO role is a varied and complex position that can incorporate data infrastructure, data governance, data security, business intelligence, insight and advanced analytics. Just as it is important for a CDO to be technically competent, it is also essential that the CDO is able to understand and guide the company objectives and incorporated change management processes, in order to deliver on this vision. 9. Campaign Analyst / CRM Analyst Loyalty programmes, web analytics and Internet of Things (IOT) technologies have led to a vast influx of customer data, which progressive companies are using to support their strategic growth plans. Marketing departments in particular are being challenged to deliver more relevant, targeted campaigns that take advantage of this data. Campaign analysts utilise their expertise in Excel and data analytics tools like SQL to provide greater customer segmentation, thereby ensuring that digital marketing campaigns meet the targeted customer base. When paired with campaign management software like Adobe Campaigns a company can ensure that their marketing strategies hit the mark. 8. Data Engineer As trendy as Hadoop and unstructured data warehousing is in today’s big data world, the first priority for any analytics function is in getting the basics right. Business Intelligence and Data Science starts with having clean, organised and usable data structures; often run through SQL Server, Oracle or SAP databases. A quality engineer with expertise in data management and ETL processes will remain a primary need for many organisations. In reality, many CDO’s could even argue that this plays a more important role than its big data sibling â€" refer to point four, big data engineer. 7. BI Developer BI developers, in its simplest form, manage the process of delivering structured data from data warehouse structures to its end users in the form of dashboards and reports. Once the land of commercial finance, Business Intelligence has now evolved into its own department, with many BI teams now prioritising the building of self-service dashboards. In doing this, they allow operational managers the chance to quickly and neatly pull key performance data to review performance. The most common technologies within the BI landscape lie with major technology giants including the Microsoft BI package (SSIS/SSAS/SSRS/PowerBI), Oracle (OBIEE, OBIA), SAP (Business Objects) and IBM (Cognos). 6. Visualisation OK, this probably should have gone in the column above, but with the proliferation of dashboard and visualisation tools, ‘front end’ BI developers with expertise in Tableau, Qlikview/QlikSense, SiSense and Looker are in increasingly high demand. Developers that have utilised d3.js in building visualisations on web browsers are also growing in popularity. Salaries in major business districts can surpass £75k a year with daily rates exceeding £500 per day. 5. Software Developer Wait, what? This isn’t a data job! The rise of big data has led to a direct increase in companies building web based applications on top of big data platforms. Balancing traditional software development tools, including Javascript, C# and PHP with Python frameworks like Django, Pyramid or Flask has become commonplace. 4. Big Data Engineer As noted above, a data engineer owns the collecting, storing and processing of a company data in order to facilitate its analysis. Historically this has involved the use of relational databases to manage data that can be stored in a tabular way-yet this often does not go far enough. Defining when data becomes big data is a much discussed topic. However for this purpose we will emphasise the mix of structure and unstructured data (image, video, audio files etc.), sometimes gathered in real-time, that is too complex to be handled by traditional structures. Big data engineers will build and maintain structures that can handle large, heterogeneous data sets often in NoSQL databases such as MongoDB. Many companies utilise a Hadoop framework with a variety of Hadoop based sub-packages such as Hive (data warehousing), Pig (data flow language) and Spark (a diverse programming model) though the list of big data infrastructure solutions is considerable. 3. Insight Analyst Whilst the name can vary from company to company, there is no denying the ever-booming demand for technically proficient analysts who can create actionable insight. Typically working within or close to product and marketing departments, insight analysts use statistical programming tools to interrogate large data sets with the goal of delivering analysis to support with customer acquisition or customer retention strategies. From a technical perspective, insight analysts will have expertise across one or more statistical programming tools. Traditionally this has meant SQL, SAS or SPSS. However more companies are looking at how R and Python can deliver greater depth of analysis and, when paired with support packages (]such as RStudio). can also include dynamic visualisations. 2. Data Architect Operating within the big data landscape is one thing. Building a big data Infrastructure is quite another. From understanding data storage in the cloud with AWS, Azure and Google Cloud, to designing an infrastructure to manage unstructured data with Hadoop or NoSQL databases an exceptional data architect can provide the foundations for a cutting edge big data solution. 1. Data Scientist Glassdoor recently called the data scientist the ‘#1 job in America’ and as the resident rock stars of the data world the role even comes with a healthy amount of discussion around what and who really classify as a data scientist. Whilst that debate rages on, the fundamentals include a strong academic background (PHD or Masters) within Statistics, Mathematics, Physics or Economics, and deep expertise in Statistics, Data Mining or Machine Learning. A quality data scientist will identify and solve highly complex business problems, utilising advanced analytics principles and tools including statistical programming in Python, R or Spark. This analysis will play a central role in decision making, providing the required intelligence to ensure that companies can successfully navigate through an increasingly complex business environment. Hopefully you found this blog interesting. Here are some related articles which you also might enjoy: Could you help reduce the digital skills deficit? Why digital transformation is not just an IT issue Top 10 emerging coding trends you need to know about How high tech cities are boosting productivity and attracting talent Why the ‘job for life’ mentality no longer exists in IT If youre looking for jobs in data, or looking to expand your data team, get in touch with your local Hays office here. Join our LinkedIn Group Join our LinkedIn Group to share your thoughts and stay up-to-date with the latest on business, employment and recruitment news in the IT industry. Join our Group Share this blog: