The defining characteristic of a strong data analyst CV is business impact — not the analyses that were run, but the decisions that changed as a result of them. "Created dashboards and reports for the sales team" is the default data analyst bullet, and it appears on thousands of applications. It says nothing about whether the dashboard was used, which decisions it informed, or whether the sales team's performance improved. Hiring managers for data analyst roles in 2026 are reading CVs with one question: did this person's analysis actually move a business metric? CVs that answer yes — with specific metrics, named stakeholders, and stated outcomes — get shortlisted at dramatically higher rates than CVs that describe technical activity without connecting it to business results. This guide covers the ATS keywords a data analyst CV needs, the structure that works for this role, and the specific mistakes that get strong candidates filtered out before anyone reads their SQL experience.

What Data Analyst Job Descriptions Require in 2026

The data analyst role covers a wider range of seniority and technical expectations than almost any other title in tech. At smaller companies and agencies, a data analyst may spend most of their time in Excel and Google Sheets. At product-led companies and scaleups, they are expected to write complex SQL, work with cloud data warehouses (Snowflake, BigQuery, Redshift), and build Tableau or Power BI dashboards from scratch. Reading the JD carefully to understand which end of that range you are applying for is critical — a CV built for a Tableau-heavy product analytics role will look overspecialized to a small business hiring for Excel-based reporting, and vice versa.

Requirements that appear across most data analyst JDs regardless of company size:

  • SQL — not just SELECT and WHERE, but JOINs across multiple tables, aggregation, window functions (ROW_NUMBER, RANK, LAG/LEAD for cohort and retention analysis), and CTEs for readable complex queries. SQL is consistently the first technical screen filter in data analyst hiring.
  • A primary BI tool — Tableau or Power BI at most mid-to-large companies; Looker for Google-stack companies; Metabase or Redash at smaller startups. The specific tool matters — an ATS filtering for "Tableau" will not surface a CV that lists only "Power BI."
  • Excel or Google Sheets — still required at many companies, particularly those with non-technical stakeholders who consume reports in spreadsheet format. Advanced Excel (pivot tables, Power Query, VLOOKUP/XLOOKUP, SUMPRODUCT) is a differentiating signal at companies where this is the primary tool.
  • Python (increasingly expected at tech companies) — pandas for data manipulation, Matplotlib/Seaborn or Plotly for visualisation, and basic automation scripting. Listed as "preferred" in many JDs; "required" at data-mature companies.
  • Stakeholder communication — presenting analysis findings to non-technical audiences, translating data insights into plain business language, and making recommendations rather than just reporting observations.

Data analyst salaries in 2026: £30K–£55K UK; $60K–$110K US, with senior data analysts at large consumer tech companies reaching £65K+.

ATS Keywords for a Data Analyst CV

Greenhouse, Lever, and Workday all filter data analyst CVs by tool and method keywords. The tools you do not list are the jobs you do not get — even when the underlying skill is identical under a different name.

Essential ATS terms for a data analyst CV:

  • Title variants: Data Analyst, Business Analyst, BI Analyst, Reporting Analyst, SQL Analyst, Marketing Analyst, Product Analyst, Analytics Engineer
  • Core tools: SQL, Python, Excel, Google Sheets, Tableau, Power BI, Looker, Metabase
  • Cloud data platforms: Snowflake, BigQuery, Redshift, dbt, Databricks, Fivetran
  • Methods and practices: A/B testing, cohort analysis, funnel analysis, statistical analysis, data visualisation, dashboard design, KPI reporting, data modelling
  • Supporting tools: Jira, Confluence, Google Analytics, Mixpanel, Amplitude, Segment
  • Long-tail phrases: data analyst resume examples, how to write a data analyst resume, data analyst cv template, data analyst skills for resume, SQL data analyst resume

Placement: SQL and your primary BI tool (Tableau or Power BI) belong in your CV headline alongside your title. "Data Analyst | SQL · Tableau · Python · Snowflake" immediately signals the right keyword cluster to the ATS and to any recruiter who glances at your header. BI tool must appear in both your headline and your Skills section to match filtered searches.

Data Analyst CV Structure and Bullets That Prove Business Impact

Section order:

  1. Headline — "Data Analyst | SQL · Tableau · Python · Snowflake" or "Data Analyst | Power BI · SQL · Excel · Google Analytics"
  2. Skills — layered: Languages and Tools / BI and Visualisation / Data Platforms / Methods
  3. Experience — 4–5 bullets per role; business decisions and outcomes here
  4. Education — degree in Statistics, Mathematics, Economics, or Computer Science; relevant online certifications (Google Data Analytics, AWS Certified Data Analytics) below education

One to two pages; one page for under 3 years of experience.

The key structural difference in data analyst bullet points compared to other roles: business outcome beats technical description every time. Three examples at the level that gets candidates shortlisted:

  • Designed and built a customer retention dashboard in Tableau using Snowflake SQL queries across 6 tables, tracking cohort retention across 12 customer segments; dashboard was adopted by product and customer success teams and directly informed 2 feature prioritisation decisions that reduced 90-day churn by 8%
  • Automated 6 weekly executive reports previously requiring 4 hours of manual Excel processing by building Python scripts with pandas and scheduled Snowflake queries, saving 24 analyst hours per month and eliminating 3 recurring error types caused by manual data entry
  • Analysed a pricing A/B test across 80K customers over 8 weeks — designed the segmentation, ran the significance testing, built the revenue model, and presented to the CMO and CFO with a clear recommendation that was adopted, generating £420K in projected incremental annual revenue

Data analyst interviews include: a live SQL challenge (typically 2–4 queries, often with a window function or multi-step aggregation), a case study ("how would you analyse this business problem"), and a communication exercise (presenting an analysis finding to a non-technical stakeholder). Your CV's outcome-based bullets set the business context for the case study you receive — production evidence of A/B test analysis or dashboard work leads to more sophisticated interview problems.

Three Data Analyst CV Mistakes That Get You Filtered Out

No BI tool in the headline. Data analyst ATS searches are frequently tool-specific — "Tableau analyst," "Power BI developer," "Looker data analyst." A CV that lists Tableau only in the Skills section, and not in the title or headline area, may not match these filtered searches. The fix: add your primary BI tool to your professional headline. It changes how both ATS and human reviewers categorise your application.

Business outcomes absent from every bullet. "Built dashboards," "created reports," "analysed customer data," "supported the marketing team with data" — these describe activities, not achievements. Every data analyst does these things. The bullet that gets attention describes what the analysis enabled: which decision it informed, how many people adopted the dashboard, what the quantified outcome was. Even a modest outcome ("analysis led to the removal of a product feature that was causing 12% of customer support tickets") is more compelling than a description of the analytical process.

Excel listed without qualification. "Microsoft Excel" or just "Excel" appears on the vast majority of data analyst CVs. It means nothing on its own — everyone who has used a computer in the last 20 years has used Excel. "Advanced Excel (pivot tables, Power Query, VLOOKUP/XLOOKUP, data validation, conditional formatting)" or "Excel for financial modelling including scenario analysis and sensitivity tables" tells a recruiter what you actually know. Specifying your level turns a generic skill into a differentiating one.

If you are applying to data analyst roles and want your CV rebuilt around the specific SQL, Tableau, and Python requirements in a target job description, Resumegpt generates your data analyst CV from your work history in under 60 seconds — keyword-matched to the JD, ATS-optimised, and exported as a PDF ready to submit.