CV Keywords for Data Analyst: SQL and Power BI in Context

Most data analyst CVs contain the right words in the wrong place. "SQL, Power BI, Excel, Python" sits in a skills box, and the experience section says "responsible for reporting". This guide covers the CV keywords for data analyst roles that tend to appear in job ads. It also shows how to prove each one inside a real bullet point, so a recruiter searching for "SQL" finds evidence, not just a label.

One rule runs through everything below: only add a keyword if it truthfully describes what you have done. A keyword you cannot talk about in an interview is a liability, not an asset.

Why a bare skills list is not enough

Applicant tracking systems (ATS) mostly store and organise applications. Many let recruiters search or filter candidates by terms such as "Power BI" or "SQL Server". Some employers also add knockout questions, such as right to work. The system usually does not "read" your CV and judge it the way a person would. For a fuller explanation, see Does ATS Automatically Reject CVs? The Honest Answer.

That means two audiences matter:

• The search: the keyword needs to be present, spelled the way the employer spells it.
• The human: once your CV appears in results, a recruiter or hiring manager wants to see what you did with SQL or Power BI, at what scale, and why it mattered.

A skills list helps with the first audience. Only context helps with the second.

CV keywords for data analyst roles, grouped

Use this table as a menu, not a shopping list. Tick only what you have genuinely used.

Group	Example keywords	Best place to show it
Query languages and databases	SQL, T-SQL, PostgreSQL, MySQL, SQL Server, BigQuery, Snowflake	Experience bullets, skills section
BI and visualisation	Power BI, DAX, Power Query, Tableau, Looker Studio, dashboards	Experience bullets, projects
Spreadsheets	Excel, pivot tables, Power Pivot, XLOOKUP, Google Sheets	Experience bullets
Programming	Python, pandas, R, Jupyter	Projects, experience
Techniques	data cleaning, data modelling, joins, window functions, ETL, A/B testing, forecasting, statistical analysis	Inside bullets, describing how
Business context	KPI reporting, stakeholder management, requirements gathering, data quality, ad hoc analysis	Profile and bullets
Domain	finance, retail, healthcare, marketing analytics, supply chain	Profile, job titles, bullets

Technique words such as "window functions" or "data modelling" are often what separate a junior CV from a mid-level one. They are also the easiest to claim falsely, so be strict with yourself.

Find the keywords this specific job uses

Every ad phrases things differently. One says "Microsoft Power BI", another "PowerBI", another "Power BI Desktop and Service". One asks for "SQL", another for "T-SQL on SQL Server". Where your experience matches, mirror the employer's exact wording at least once.

A quick routine:

1. Copy the ad into a plain document.
2. Highlight every tool, technique and business term.
3. Mark which appear in the "essential" list versus "desirable".
4. For each essential term you genuinely have, find a bullet where you can show it in context.
5. For each one you do not have, leave it out, or mention honest adjacent experience (for example, Tableau when the ad says Power BI).

For a more thorough method, read How to Find Keywords in a Job Description: A 4-Pass Method.

Showing SQL in context

A useful bullet formula is:

Action verb + SQL specifics + data source or scale + purpose + result (only if you can verify it).

Before and after examples

Weak: "Used SQL for reporting."

Stronger: "Wrote SQL queries in SQL Server, using joins across sales, customer and returns tables, to build a weekly returns report for the operations team."

Weak: "Advanced SQL skills."

Stronger: "Rewrote a monthly churn query using window functions and CTEs, replacing a manual Excel process the team previously ran by hand each month."

Weak: "Data extraction."

Stronger: "Extracted and cleaned transaction data from PostgreSQL, handling duplicates and missing values, before analysis in Python (pandas)."

Notice what the stronger versions do. They name the database, the technique and the business purpose. A reader can picture the work and prepare sensible interview questions about it.

Match the depth to your real level

• Beginner (courses, projects): SELECT, WHERE, GROUP BY, basic joins. Say so plainly: "Wrote SQL queries with joins and aggregations to analyse a public bike-share dataset."
• Intermediate: CTEs, subqueries, window functions, views, query performance.
• Advanced: stored procedures, indexing decisions, data warehouse design, dbt models.

Do not write "advanced SQL" if you would struggle to explain a window function. Interviews for analyst roles often include a live or take-home SQL test, so overstating tends to surface quickly.

Showing Power BI in context

Power BI has its own vocabulary, and using it accurately signals real experience. Useful terms include DAX, Power Query, data model, star schema, relationships, row-level security, Power BI Service, scheduled refresh and workspaces.

Before and after examples

Weak: "Created Power BI dashboards."

Stronger: "Built a Power BI sales dashboard for regional managers, combining SQL Server and Excel sources in Power Query and writing DAX measures for year-on-year growth."

Weak: "Experience with data visualisation."

Stronger: "Redesigned a 12-page Power BI report into a 3-page summary with drill-through, after gathering requirements from finance stakeholders."

Weak: "Power BI Service."

Stronger: "Published reports to Power BI Service, set up scheduled refresh and applied row-level security so each branch saw only its own data."

If you mention a result, such as time saved or wider adoption, make sure it is something you could explain and, ideally, that a former manager would recognise. If you do not know the number, describe the change instead of guessing.

Combining SQL and Power BI in one bullet

Hiring managers often want to know whether you can work end to end. One bullet can show the whole pipeline:

"Wrote SQL views to prepare inventory data, modelled it in Power BI as a star schema, and built a stock-level dashboard the warehouse team used in daily stand-ups."

That single line covers SQL, data modelling, Power BI and stakeholder use, without a single empty buzzword.

Where keywords should appear on your CV

Spread keywords across sections so they read naturally:

• Profile (3-4 lines): your level, domain and two or three core tools. Example: "Data analyst with three years in retail reporting, working mainly in SQL Server and Power BI."
• Skills section: grouped and honest, not a wall of logos.
• Experience: where most of the context lives.
• Projects: essential if your SQL or Power BI experience comes from study or self-directed work.

A simple skills template:

Data and querying: SQL (SQL Server, PostgreSQL), Excel (pivot tables, Power Query) BI and visualisation: Power BI (DAX, Power Query, Service), Tableau (basic) Programming: Python (pandas, matplotlib) Methods: data cleaning, KPI reporting, A/B test analysis

Labels such as "(basic)" are not weakness. They make the rest of the list more believable.

If your experience comes from courses or projects

Many career changers and graduates learn SQL and Power BI outside work. That is fine, but present it as what it is.

• Create a Projects section above or alongside experience.
• Name the dataset (for example, a public government or open data source) and the question you answered.
• Link to a portfolio, GitHub repository or published Power BI report if you have one.
• Do not place project work under a job title as though it were paid employment.

Example: "Analysed public transport punctuality data using SQL (PostgreSQL) and built a Power BI report comparing routes by month; report and queries linked on GitHub."

You can also show analytical work from non-analyst jobs, such as an Excel tracker you built as an administrator, as long as you describe it accurately.

Country differences to keep in mind

The keywords themselves are broadly global, but conventions around them differ.

• UK: "CV", usually around two pages, British spelling in your own sentences ("analyse", "visualisation"). Keep tool names exactly as the vendor writes them. If the ad uses US spelling, matching it once is reasonable.
• US: "Resume", often one page for early-career candidates, US spelling ("analyze"). Skills sections tend to be concise.
• Canada: "Resume", no photo or personal details such as date of birth. Projects and volunteering help if you lack local experience.
• Australia: "Resume", often two to three pages. Public sector roles may ask you to address selection criteria separately, where your SQL and Power BI examples can become STAR or CAR stories.
• Nigeria: "CV" is standard. Platforms such as Jobberman may parse your document, so keep formatting simple; see ATS Friendly CV Format for Nigeria and Jobberman. For international remote roles, check location eligibility in the ad before tailoring.

Conventions vary by employer too, so treat these as starting points rather than fixed rules.

A 10-minute keyword check before you apply

1. Every essential tool in the ad that you genuinely have appears at least once, spelled the employer's way.
2. SQL appears in at least one experience or project bullet, not only the skills list.
3. Power BI (or the BI tool named) appears with specifics such as DAX, Power Query or Service.
4. Each technique keyword (joins, window functions, data modelling) is one you could explain in an interview.
5. Skill levels are labelled honestly where relevant.
6. Projects are clearly labelled as projects.
7. Spelling and CV length suit the country you are applying to.
8. Your CV is in a simple format that copies cleanly into plain text.

If you want a second view, the QuickApplyPilot ATS CV checker compares your CV with a job ad and highlights missing terms. Its score is QuickApplyPilot's own estimate of the match, not the score any employer's ATS would give, and it cannot guarantee an interview. Use it to spot gaps, then fill only the ones that truthfully reflect your experience.