Data & Analytics
Gravitas has over 14 years' experience as Data & Analytics recruitment specialists, building relationships with businesses across the Public Sector, Private Sector and Non-Profit sectors, including Greentech, Insurtech, Healthtech, and Fintech. We work with tech professionals working across Data Science, Data Engineering, Analytics, AI & Machine Learning, Data Governance & Quality and more.
If you are looking to work with businesses that make an impact, provide purpose, and help make a difference, contact our team.
Our candidate community can expect excellent career development advice, along with guidance on the whole hiring processes. We will help match you with some of the most exciting and interesting tech jobs in the industry, supporting you through the application and interview process to help you get hired, fast!
Top Data & Analytics jobs in demand
Our clients are looking for talented tech professionals across the Data & Analytics specialism.
Data Science
Data Science has been shaping the way organisations use data insights for years, resulting in a better understanding and the capability to interpret large amounts of data (Big Data) to gain optimum outcomes.
Combining a multitude of expertise, Data Science uses statistics, mathematics, algorithms and data methods to extract information. Some of the key specialists our clients are looking for, include:
- Data Scientist
- Machine Learning Engineer
- Big Data Engineer
Within these roles, common skills like programming (i.e. Python, SQL, R), machine learning, statistical analysis and data mining are in demand.
Artificial Intelligence & Machine Learning (AI & MI)
As one of the biggest areas within IT transformation, AI and MI are rich with opportunity for career development in the following roles:
- Data Scientist
- AI Specialist
- Machine Learning Engineer
AI and MI, as part of Data Science, gives organisations the ability to optimise the automation of processes through efficient data collection and in-depth & meaningful data insights. They may focus on enabling computers or intelligent systems to learn and make predictions from data, or incorporate reasoning, problem-solving and decision-making.
Data Engineering
Data Engineering involves building systems or suites, where raw data reports can be transformed into usable datasets for analysis and insights. A key part of this is the maintenance, testing or optimisation of systems.
Data Engineers are the individual specialists in this area, but can vary in responsibility such as building an on-premise Microsoft Datawarehouse or someone building streaming data pipelines using Apache Kafka As businesses are increasingly digitalising their infrastructure, there is an increasing demand for skills across AWS, Azure and other cloud platforms across all levels, from Junior to Head of positions.
Data Integration Engineers may also work with Data Engineers to coordinate changes to computer databases through administration and testing.
Data Analytics
Data Analytics is the process of examining and interpreting data, to help organisations understand performance insights and guide decision making. Data Analysts are the specialists in this field, producing regular reports, monitoring metrics, and identifying trends or patterns.
Business intelligence skills are the foundation of their day-to-day role, using statistical analysis, data modelling or data visualisation tools (i.e. Power BI, Excel, Tableau) to interpret data.
Data Protection, Data Governance & Quality
When organisations collect and use data, there are a number of data protection policies to follow, in order to protect their employees, customers and providers in the privacy of their data.
Data quality measures how well data meets the criteria for accuracy, validity and its fit for purpose. Some organisations may use data quality tools (i.e. Informatica, Collibra, Tibco). Data Governance is the actioning of policies which apply to how data is collected, gathered, stored, processed and disposed of.
GDPR requires organisations to have key individuals to ensure that the data being processed is compliant to data protection policies, such as:
- Data Protection Officer
- GDPR Officer
These roles often act as subject matter experts in policies to follow and implementing procedures across the business.
Top tips for a career in Data & Analytics
Expert advice from Associate Director, Josh Taylor
Is it important to be a specialist in Data?
Data is a flexible career, where there are lots of opportunity to learn different skills and explore specialisms, no matter where you are in your career. If you are early in your career in Data, consider researching and exploring different specialist areas you can potentially go into. You can get an idea of what you enjoy and what you could expand on later in your career.
Larger companies have big data teams with specialists in each data discipline, whereas startups benefit from having data specialists with broad skillsets. See what type of organisation you think would suit your long-term goals and align your career development plan alongside this.
Think about how your work fits into the bigger picture
Always consider how your work fits in the organisation’s strategy. How does the work you are doing contribute to the overall business strategy? Learning this can allow you to work better across the organisation, whether that's cross collaboratively with other departments, stakeholders or project managers. Many interviews for data positions involve questions around data governance or data quality to see if you understand business impact, so be proactive in learning! Our clients have commented that the most successful appointments in data are individuals who are inquisitive, having both an understanding of data but also an understanding of the business needs.
Make data interesting for everyone!
Whilst you are passionate for data and enjoy the work that you do, unfortunately, not everyone shares the same excitement. Your role can go further in making the subject matter more interesting for stakeholders, and selling the benefit of what data can do for them. By using story-telling techniques and visual aids can make it easier to understand and accessible for those who aren’t experts in data. Learn more to be concise when communicating, such as avoiding technical jargon, and you can bring others along on a ‘data journey’.