Data Analyst Resume Keywords: SQL, BI Tools and Domain
Data analyst screening is unusually predictable. One term appears in almost every posting and functions as a knockout, a second term is tribal and unforgiving, and a third — the one most applicants leave out entirely — decides which shortlist you land on. Get SQL, the BI tool and the domain right and the rest of the page is a tie-breaker.
SQL is the knockout, and it has to be visible as three letters
SQL is the most common hard requirement in analyst postings and one of the cheapest keywords to be missing. It has to appear as the bare string, not only inside “Microsoft SQL Server” or “PostgreSQL”, because an exact-token search for SQL will not always reach inside a longer product name. Put it first in the skills block and then show it in a bullet.
Depth is worth signalling because everyone claims the word. Window functions, common table expressions, query optimization and stored procedures are the terms that separate someone who writes queries from someone who has been handed a SELECT statement. If you have tuned a slow query or rebuilt a reporting table, that is the bullet.
Name the warehouse as well as the language. Snowflake, BigQuery, Redshift, Databricks and Azure Synapse are separate searches, they imply different scale and cost habits, and postings name them directly. So do the older stacks: SQL Server and SSRS still appear across large sections of UK enterprise and public sector hiring, and there is no shame in either.
Tableau and Power BI are tribes, not synonyms
No search for Tableau will ever return a resume that says Power BI. This matters more than the equivalent problem elsewhere because the two tools split the market almost geographically: Power BI dominates UK, European and Microsoft-shop hiring on the back of licensing that comes bundled with Office, while Tableau holds more ground in US tech and in analytics-first teams. Looker and Qlik hold real but smaller shares, and Looker Studio is a different product from Looker.
Inside each tool there are sub-skills that are searched separately and that prove you have gone past drag-and-drop. DAX and Power Query are the two that matter for Power BI, and both appear in postings as standalone requirements. LOD expressions and Tableau Prep do the same job on the other side. LookML is the equivalent for Looker.
“Data visualisation” as a phrase is worth having on the page because postings use it, but it will never substitute for the product name. Write both, and if you have genuinely used two tools, say which one you would call yourself fluent in rather than listing them as equals.
The domain noun is what gets you on the shortlist
Two analysts with identical SQL and identical Power BI are separated by what they analyzed. Marketing analytics screens for attribution, channel performance, GA4, cohort analysis and CAC. Product analytics screens for funnels, retention, Amplitude or Mixpanel, and experiment analysis. Finance screens for variance, forecasting and reconciliation. Supply chain screens for demand planning, inventory turns and OTIF. Healthcare screens for the coding systems. None of these vocabularies overlaps, and the hiring manager is looking for the one they recognise.
Say who you served as well as what you analyzed. “Built the weekly trading pack for a £40m e-commerce business” names an industry, a cadence, a stakeholder and a scale in twelve words, and a generic “created dashboards for stakeholders” names nothing.
Experimentation vocabulary is worth singling out because it is now screened for at mid-level and above in product-facing roles: A/B test, statistical significance, sample size, holdout, incrementality. Only claim it if you have designed or read out a test — the interview will go there immediately.
Which analyst keywords are doing the screening
A data analyst posting typically lists fifteen to twenty requirements. These are the ones that behave like filters rather than preferences.
- SQL
- Present in the overwhelming majority of postings and used as a hard filter. Write the bare three letters as well as the product name.
- Power BI or Tableau, named
- Tribal and non-substitutable. Naming the wrong one is close to naming neither, so lead with the tool your target market uses.
- Excel, with the functions
- Still screened for, still underrated. “Advanced Excel” means nothing; PivotTables, XLOOKUP, Power Query and array formulas mean something.
- Python, only if it is real
- pandas, NumPy and matplotlib are the strings that make the claim credible. A completed course is not experience and the take-home test will show it.
- The data warehouse
- Snowflake, BigQuery, Redshift or Databricks tells a hiring manager what scale you have worked at more reliably than any adjective.
- Data cleaning and transformation
- The bulk of the actual job and rarely written down. Postings phrase it as data wrangling, data quality or ETL; use whichever the posting uses.
- dbt
- The fastest-growing analyst keyword of the last few years and the one that moves you into analytics engineering pay bands. Include it if true.
- Stakeholder management
- Sounds like filler and is not. Analyst postings are explicit about requirements gathering and presenting to non-technical audiences, and it is searched.
ATS keywords for a Data Analyst Resume
Use these as a checklist — include the ones that genuinely apply to you, matched to the wording of the job you are targeting.
Core skills
Tools & software
Soft skills
Certifications & qualifications
The four titles a data analyst resume sits between
This corner of the market is crowded with near-synonyms that mean different jobs at different salaries. Choose deliberately, and put the adjacent title on the page only if you would take that job.
- Data Analyst
- The broadest term and the highest search volume. Correct default unless your work is clearly one of the specialist areas below.
- Business Intelligence Analyst
- Signals reporting infrastructure, semantic layers and dashboards over ad-hoc analysis. Common in enterprises with a dedicated BI function.
- Analytics Engineer
- dbt, warehouse modeling and version-controlled transformations. A distinct and better-paid market; claim it only with the tooling to back it.
- MI Analyst
- Management information. Almost exclusively UK, and heavily used in financial services, insurance and the NHS. Invisible to US recruiters, essential to some UK ones.
- Insight Analyst / Reporting Analyst
- UK retail, charity and public sector phrasing for the same job. Worth carrying if you are applying into those sectors.
- Data Scientist
- Do not reach for it to sound senior. It shifts your page into screens that test statistics and machine learning, and analyst experience alone will not clear them.
How to get a Data Analyst Resume past the ATS
- Name every tool from the posting by name — "SQL, Python, Power BI" — never substitute a category like "BI tools".
- Show the query and visualisation layer separately: a SQL skill and a Tableau/Power BI skill are matched as different keywords.
- Quantify the decision your analysis drove (revenue, cost saved, conversion lift), not just the volume of data handled.
- Include statistical methods named in the role — "A/B testing", "regression", "forecasting" — if you have used them.
- Add a plain-text "Technical Skills" line; ATS parsers read it more reliably than icon-based skill ratings.
What makes a competent analyst look junior on paper
Counting dashboards instead of decisions
“Built 30 dashboards” describes activity. “Replaced a weekly manual report with a Power BI model, freeing two days a month and changing how the trading team set promotions” describes a result, and contains more searchable terms than the first version.
Claiming Python from a course
Analyst technical tests are short and specific. If your Python is a certificate rather than a project, list it as familiarity, put your SQL depth forward instead, and you will pass more screens rather than fewer.
“Data-driven” as a skill
It appears on a very large share of analyst resumes, matches nothing a recruiter searches, and takes the place of a tool name. The same is true of “data storytelling” unless you attach it to a specific audience and outcome.
No sense of scale anywhere on the page
Row counts, refresh frequency, number of source systems, number of report consumers. Any one of them tells a hiring manager whether you have worked at their size, and most analyst pages contain none of them.
Before & after: Data Analyst Resume bullets
Before: Built reports for the marketing team.
After: Built 12 self-serve Power BI dashboards from SQL data sources for the marketing team, cutting ad-hoc reporting requests by 60%.
Before: Analysed customer data to find insights.
After: Analysed 2M+ customer records in SQL and Python, identifying a churn driver that informed a retention campaign worth £180k in saved revenue.
Free Data Analyst Resume template
Every keyword on this page, already in the section a parser expects to find it in. Fill in the bracketed fields and you have a Resume an ATS can read.
Data Analyst Resume keywords — FAQ
Do I need Python to get a data analyst job?
For most analyst roles, no — SQL plus a BI tool clears the majority of postings, and plenty of well-paid analysts never write Python. It becomes close to mandatory in product analytics, in any role touching experimentation, and in teams where the analyst owns their own pipelines. If you are targeting those, learn pandas properly rather than listing the word.
Should I learn Tableau or Power BI?
Look at where you want to work rather than at which is better. Power BI appears in far more UK, European and enterprise postings because it arrives with a Microsoft license the employer already holds. Tableau is stronger in US technology companies and in organizations with a mature analytics function. Learn the one your target postings name, and put it on the page as the product name.
Is Excel still worth listing in 2026?
Yes, and leaving it off is a small unforced error. A meaningful share of analyst postings still name it explicitly, and in finance, operations and the public sector it is the primary tool rather than a legacy one. The way to list it without sounding dated is to name the functions — Power Query and XLOOKUP read as current, “Microsoft Excel” alone reads as 2009.
How do I move from business analyst to data analyst on paper?
The two titles are close enough to confuse a recruiter and far enough apart to fail a screen. Rewrite your bullets around the data verbs — queried, built, analyzed, reported and validated — rather than the process verbs, put SQL and your BI tool at the top of the skills block, and lead with any piece of work where you produced the numbers rather than gathering the requirements for someone else to produce them.
