What Data Analyst Employers Look For
Hiring managers reviewing data analyst applications want to see: the technical stack you work with (SQL, Python, R, Tableau, Power BI), the scale and complexity of data you have handled, the business decisions your analysis has influenced, and your ability to communicate insights to non-technical audiences.
Professional Summary for a Data Analyst
Example (mid-level):
Data analyst with 4 years of experience in e-commerce and retail analytics. Proficient in SQL, Python (pandas, matplotlib) and Power BI. Built dashboards and ad-hoc analyses that directly informed £15M+ in product and merchandising decisions. Strong communicator — experienced translating complex data outputs into clear recommendations for senior leadership.
Example (senior):
Senior data analyst with 8 years of experience across fintech and insurance. Expert in SQL, Python and Tableau. Led the analytics function through a migration from legacy reporting to a cloud-based data warehouse (Snowflake + dbt), reducing report generation time from 2 days to 4 hours. Experienced leading a team of 3 junior analysts.
Technical Skills Section
Group your tools by category for maximum scannability:
- Query languages: SQL (PostgreSQL, MySQL, BigQuery, Redshift), dbt
- Programming: Python (pandas, NumPy, matplotlib, seaborn), R
- BI and visualisation: Tableau, Power BI, Looker, Google Data Studio
- Cloud and data platforms: Google BigQuery, AWS Redshift, Snowflake, Databricks
- Other tools: Excel (advanced), JIRA, Confluence, Git
- Statistical methods: A/B testing, regression analysis, cohort analysis, forecasting
How to Write Data Analyst Bullet Points
The key: every bullet should show what decision was enabled or what outcome was achieved — not just what you built.
- "Built a customer churn prediction model using Python and logistic regression, identifying 1,200 at-risk accounts that generated £340K in retained revenue through targeted outreach"
- "Automated weekly KPI reporting using SQL and Power BI, eliminating 8 hours of manual work per week and reducing report delivery time from Monday to Friday"
- "Designed and analysed A/B test for homepage redesign across 450,000 users — results informed a decision that increased conversion rate by 1.8% (worth ~£280K ARR)"
- "Created a self-service analytics dashboard used by 60+ non-technical stakeholders to monitor performance against quarterly OKRs"
Projects and GitHub
Include a projects section if you have personal or academic projects that demonstrate relevant skills — especially if you are early in your career. Link to a GitHub profile or portfolio if it contains well-documented, current work. A Kaggle competition placement or a published Tableau public dashboard also adds credibility.
Data Analyst vs Data Scientist — CV Differences
- Data analyst: Emphasis on SQL, BI tools, business reporting, descriptive analytics, stakeholder communication
- Data scientist: Emphasis on machine learning, Python/R, modelling, statistical methods, research
Make sure your CV clearly positions you in the right category — the overlap causes confusion for ATS systems and human reviewers alike.
ATS Keywords for Data Analyst Roles
Common keywords that appear in data analyst job descriptions: SQL, Python, R, Tableau, Power BI, data visualisation, A/B testing, Excel, statistics, data cleaning, ETL, business intelligence, stakeholder management, KPI reporting, data modelling, Snowflake, BigQuery.