Data
Data Analyst CV Template UK
Data analysts in the UK are embedded across commercial, product, operations, and finance teams, turning raw data into decisions that move the business. Employers look for candidates who can demonstrate technical confidence in SQL and visualisation tools alongside the ability to translate findings into clear business recommendations. The strongest applications show not just what was analysed, but what decision or improvement resulted from the work. Whether you work in a startup with a single analytics stack or a large enterprise with complex data infrastructure, your CV should reflect the business context of your analysis.
The UK data analyst market in 2026 is splitting into two distinct tracks: traditional BI/reporting analysts (Power BI, SQL, Excel) and product/growth analysts who write SQL against event data in Snowflake/BigQuery and increasingly use dbt and Python. The latter pays 15–25% more in London and grows faster. Hiring managers screen ruthlessly on real SQL — "proficient in SQL" without an example is treated as a flag. Most ATS funnels for analyst roles weight tool names and require explicit mention of the company's stack (Looker, Tableau, Power BI, dbt) to advance.
Data Analyst salary bands in the UK (2026)
Indicative UK ranges based on current market data. London and specialist sector roles typically sit at the upper end of each band.
Junior Analyst (0–2 yrs)
£30k–£42k
London tech firms pay £38k–£42k starting; regional and public sector £28k–£35k.
Analyst (2–4 yrs)
£40k–£55k
Product/growth analysts at scale-ups top end. Strong SQL + one dbt or warehouse project pushes ceiling.
Senior Analyst (4–7 yrs)
£55k–£80k
Expect to own a domain (marketing, product, finance) and influence decisions, not just produce reports.
Lead / Analytics Engineer (7+ yrs)
£75k–£110k
Analytics engineering (dbt, Snowflake, modelling) commands the upper band.
Data Analyst CV bullet examples — weak vs. strong
Real examples specific to this role. Use them as templates for rewriting your own bullets.
Weak
Built dashboards in Power BI for the marketing team and ran weekly reports.
Strong
Rebuilt marketing's attribution dashboard in Power BI on top of a new dbt model; reduced reporting time from 8 hours weekly to 90 minutes and surfaced a £140k underperforming campaign that was reallocated within the quarter.
Why it works: Names the tool, the layer of work (dbt model), the time saved, AND the business decision the new view enabled. Senior analyst screens look for decision impact, not dashboard volume.
Weak
Wrote SQL queries to support ad-hoc requests from the operations team.
Strong
Authored ~25 SQL queries/month against Snowflake supporting Ops; productionised the top three repeating requests into a self-service Looker dashboard, eliminating 12 hours/week of ad-hoc work.
Why it works: Quantifies the volume, names the warehouse, and shows the move from reactive to systematised work — a key senior signal.
Common mistake
Writing CV bullets that describe what the data showed rather than what changed because of it. "Analysed customer churn data" is invisible to an ATS scoring outcome keywords. Replace with "Identified that 60-day inactive users had 4x churn risk; recommended targeted re-engagement campaign that reduced 90-day churn by 9%."
Pro tip
Add a short "Stack" line listing your warehouse, modelling tool, BI tool, and language — e.g. "Snowflake / dbt / Looker / SQL + Python". UK analytics recruiters scan for stack alignment in 5 seconds; this single line outperforms a bulleted skills section for first-pass screening.
Next Step
Check your CV for this role before you apply
Use the ATS checker to compare your CV against a real data analyst job description, then rewrite weak sections in the AI CV builder.
What recruiters look for in a Data Analyst CV
- Proficiency in SQL and dashboarding tools backed by specific business use cases, not just tool names listed
- Ability to turn data findings into actionable recommendations that non-technical stakeholders understood and acted on
- Experience with reporting workflows: cadence, stakeholder audience, and how insights influenced decisions
- Quantified business impact such as cost savings identified, churn reduced, or revenue opportunity uncovered
- Accuracy and data quality standards — evidence of validation, governance, or improving unreliable reporting
- Domain understanding relevant to the employer's sector, whether finance, product, operations, or marketing
Seniority levels this page covers
Tailor your summary, recent experience, and keyword coverage to the level you are applying for. Senior roles usually need stronger ownership, scope, and commercial impact language.
How to make this page useful before you apply
Mirror the right language
Do not rewrite everything at once. Start by checking whether your current CV already uses the same skill and keyword language as the role, especially around SQL, Excel, Power BI.
Prove the right kind of impact
The strongest data analyst CVs do not rely on broad claims. They show concrete evidence of proficiency in sql and dashboarding tools backed by specific business use cases, not just tool names listed and ability to turn data findings into actionable recommendations that non-technical stakeholders understood and acted on.
Match your level
This page covers junior through lead analyst applications. As the level rises, your wording should show more scope, ownership, and decision quality.
Key skills to include
ATS keywords recruiters expect
ATS score tips for this role
Name the specific tools you have used — SQL, Power BI, Tableau, Python, or Excel — and pair each with a real business context rather than listing them in isolation.
Translate each analysis project into a business outcome: "identified £180k annual saving", "reduced customer churn by 12%", or "improved forecast accuracy by 20pp".
Use the employer's terminology for their data environment — if they mention Snowflake, dbt, or Looker in the JD, reflect relevant experience with those or similar tools.
Avoid framing your role purely as reporting production — show that your analysis was used for decisions, not just distributed as a dashboard.
For senior analyst roles, make stakeholder influence and cross-team collaboration visible alongside technical output, since ATS filters often include "business partnering" and "insight communication" for these levels.
Common questions about data analyst CVs
How should I tailor a data analyst CV for UK employers?
Start by matching the job description language where it reflects your real experience. For data analyst roles, employers usually look for evidence around proficiency in sql and dashboarding tools backed by specific business use cases, not just tool names listed and ability to turn data findings into actionable recommendations that non-technical stakeholders understood and acted on.
Which keywords matter most for a data analyst CV?
The strongest starting point is usually the job description itself, but recurring keywords for this role include data analyst, SQL, dashboarding. Use them where they accurately describe your work instead of forcing them into a generic summary.
What changes between junior and lead analyst applications?
Junior applications usually need clearer evidence of core execution and role fit. Lead Analyst applications normally need stronger ownership language, broader scope, and more visible commercial or organisational impact.