AI is changing the way organisations use their data, and that shift is putting data analyst talent at the centre of how teams work today.
AI systems are only as strong as the data they rely on, making data analysts a key part of ensuring that data is accurate and ready for use.
Yet, 32 percent of businesses cite a lack of specialist skills as a major barrier to their AI adoption, emphasising just how essential core data analytics skills have become for today’s AI‑driven projects.
So, what exactly are employers looking for in a modern data analyst, and what skills should you put on your CV to get noticed?
Top skills
When reviewing CVs for data analyst roles, hiring managers are looking for the core skills that show you can confidently work with data and deliver effective data analysis services.
A CV should include at least some of the below skills:
- Data Analysis
- Data Analytics
- Data Engineering
- Business Intelligence (BI)
- Data Modelling
- Data Quality and Validation
- Data Governance
- Data Lifecycle
- Analytical Problem Solving
- Data‑Driven Decision Making
Supporting technical skills
Many data analyst roles now involve working with systems that organisations use to prepare and organise their data. You don’t need to be a data engineer, but understanding some of these basics helps you work confidently and stand out more.
- ETL / ELT
- Data Pipelines
- Data Warehousing
- SQL Development
- Relational Databases
- Data Integration
- Data Transformation
Technologies
Employers also look for familiarity with the tools commonly used in analytics and data work which include:
- SQL
- Python
- Azure Data Factory
- Databricks
- Spark
Cloud and Platform
As more and more organisations move their data storage to the cloud, it’s useful for analysts to be familiar with cloud-based tools and systems:
- Cloud Data Platforms
- Azure / AWS / GCP (depending on requirements)
- Azure Data Lake
- Cloud Analytics
How to stand out to employers
When it comes to making your CV really stand out, it’s not enough to just list every tool you’ve used. What really catches an employer’s eye is seeing the impact behind those skills and how you’ve used them to make a real difference.
If you’ve made reporting clearer, helped people find information faster or used automation to save time, include those examples. They show you can turn data into actionable insights. Employer also value curiosity, so if you’ve explored AI tools to speed up analysis or support problem-solving, include that too. It shows you’re comfortable working with emerging tools and applying it in a practical way.
Advice for an entry-level data analyst
Demand for data analysts remains strong, especially as organisations rely more on AI and need trustworthy data to support it.
Evidence of problem-solving and the ability to learn quickly, are qualities that matter just as much as technical knowledge. You can show these through portfolio projects, coursework, volunteering, internships or any work where you’ve handled data and produced something meaningful.
These examples help employers see how you think, organise your work and collaborate with others, even if you haven’t held a formal analyst role yet.
By showing what you’ve built or contributed to will make your CV stand out far more than listing tools alone.
