Tailoring guide

Data Analyst resume keywords and example

Data analyst postings filter hard on tools: the SQL dialect or warehouse, the BI tool and, increasingly, dbt and Python. They also look for evidence that analysis changed a decision. Below are the keywords that repeat, a sample summary, skills grouped by type, and example bullets across seniority levels.

Updated 4 October 2026. Keyword research current as of July 2026, sourced from public ATS keyword guides, not Weydr's own usage data.

Data Analyst keywords that repeat in job postings

  • SQL
  • Python (pandas, NumPy)
  • Tableau / Power BI
  • BigQuery / Snowflake / Redshift
  • A/B Testing
  • KPI Reporting
  • ETL
  • dbt

Only include the ones that are true for you. A keyword you can't back up in an interview costs more than it gains.

Sample professional summary

Data analyst with 3 years in e-commerce and SaaS, writing SQL against BigQuery and Snowflake and building Tableau and Power BI dashboards for product and marketing teams. Designs and analyzes A/B tests, maintains dbt models for core KPIs, and turns analysis into recommendations that change roadmaps and budgets.

Illustrative example. Swap in your own years, domain, tools and results.

Skills to list on a data analyst resume

Hard skills

  • SQL (joins, window functions, CTEs)
  • Data cleaning and validation
  • Statistical analysis and A/B testing
  • Data modeling
  • KPI and metric definition
  • ETL / ELT pipelines
  • Dashboard design
  • Python for analysis

Soft skills

  • Stakeholder communication
  • Data storytelling
  • Requirements gathering
  • Attention to detail
  • Prioritization

Tools

  • BigQuery
  • Snowflake
  • Redshift
  • dbt
  • Tableau
  • Power BI
  • Looker
  • Python (pandas, NumPy)
  • Excel / Google Sheets
  • Airflow

A tailored bullet, before and after

Analyzed data and created reports for stakeholders.

Queried BigQuery datasets in SQL and built Tableau dashboards tracking activation KPIs, surfacing a drop-off point that led to a 15% signup-to-activation lift.

Example resume bullets by seniority

Illustrative examples: the companies, numbers and outcomes are made up to show the structure (action, scope, tool, result). Use your own real figures.

Entry level

  • Built a weekly Tableau dashboard of sales KPIs from Snowflake, replacing a manual Excel report and saving the team about 5 hours a week.
  • Cleaned and joined 3 years of order data in Python (pandas) to analyze repeat purchases, identifying the categories behind 60% of returning customers.

Mid level

  • Designed and analyzed 15 A/B tests with the growth team, including power calculations and guardrail metrics; 4 winning tests lifted checkout conversion by a combined 9%.
  • Rebuilt core revenue and retention metrics as tested dbt models in BigQuery, ending conflicting numbers between finance and product dashboards.

Senior

  • Led analytics for a marketing budget review, building a multi-touch attribution model in SQL that moved 20% of paid spend to channels with lower CAC.
  • Defined the company's north-star metric and KPI tree with leadership and set up the Looker reporting used in monthly business reviews.

Gaps candidates miss most

  • The SQL dialect or warehouse (BigQuery, Snowflake, Redshift) instead of just "SQL"
  • The specific visualization tool (Tableau, Power BI) instead of "data visualization"
  • A quantified outcome tied to the analysis, not just the analysis itself

Common data analyst resume mistakes

  • "Analyzed data" with no dataset, tool or decision attached.
  • Listing every tool you've touched instead of the warehouse and BI stack you actually used.
  • Describing dashboards without saying who used them or what changed because of them.
  • Leaving out statistics and experimentation when the posting mentions A/B testing.
  • Burying SQL in a long skills list when it is the most searched keyword for the role.

Before you stuff in keywords

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