With recruitment in data on the rise and big businesses investing heavily in the data sector, Hays is looking at the top jobs in data.
As a topic, data 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 predicted that 73pc of organisations would be ‘investing heavily’ in big data projects by 2016.
As we come to the end of the year, it’s a good time to review the data landscape and count down the top ten data jobs.
10. Chief data officer (CDO)
It all starts at the top, and for companies that are serious about unleashing the potential of their data footprints, appointing a CDO is an essential first step. From 400 CDOs in 2014 to over 1,000 in 2015, Gartner suggests that 90pc of the UK’s large companies will have a CDO 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 sweet spot.
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 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 CDOs could even argue that this plays a more important role than its big data sibling.
7. BI developer
BI developers, in their 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 tech giants, including the Microsoft BI package (SSIS/SSAS/SSRS/PowerBI), Oracle (OBIEE, OBIA), SAP (BusinessObjects) and IBM (Cognos).
Okay, this probably should have gone before BI developers. However, 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
4. Big data engineer
As mentioned, a data engineer manages the collecting, storing and processing of a company’s 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 structured and unstructured data (image, video, audio files etc) that is sometimes gathered in real time and 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 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, but 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 ‘number one job in America’. As the resident rock stars of the data world, the role even comes with a healthy amount of discussion around what and who really classifies as a data scientist. While 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.
Chris Taylor is a senior manager at Hays. Taylor specialises in recruiting business intelligence and data specialists on a permanent basis within commercial organisations.
A version of this article originally appeared on Hays’ Viewpoint blog.
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