You have built dashboards that decision-makers relied on, written complex SQL that surfaced trends nobody had noticed, and delivered analysis that changed strategy. But if your resume describes this work in generic terms, the ATS will rank it below candidates with shallower experience and better keyword alignment.
Data analyst roles have some of the most specific ATS filtering of any function — because the tool and technique landscape is wide and every employer has a preferred stack. An ATS configured for a Tableau shop may not score Looker experience the same way, even though the skills overlap substantially.
Writing "strong SQL skills" is nearly invisible to ATS. What scores is specificity: window functions, CTEs, subqueries, query optimisation. Similarly, BI tools must be named individually — Tableau, Power BI, Looker, Metabase, and Google Data Studio are each separate keyword searches.
Python and R must both be named if used — they are not interchangeable in ATS systems. Libraries matter too: pandas, NumPy, matplotlib, and seaborn appear in ATS configurations for analyst roles that require Python. Writing "Python for data analysis" without naming the libraries scores less than naming them explicitly.
Are your SQL techniques and BI tools named specifically in your resume?
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The second failure mode is describing analysis outputs without business impact: "analyzed sales data" scores poorly; "built weekly sales funnel report in Tableau reducing analyst time-to-insight by 60%" scores well. ATS systems at modern companies weight business-outcome language because it signals the role seniority they are hiring for.
Statistical vocabulary also matters for roles involving modelling: regression analysis, A/B testing, cohort analysis, churn modelling, statistical significance, and hypothesis testing are each keyword clusters present in analyst job descriptions. Name the methods you have used.
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The gap between a data analyst resume that gets filtered out and one that gets interviews is almost always specificity — not depth of experience. Name every tool, every SQL technique, and every statistical method. Let the ATS match what you actually know.
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The most common cause is generic tool language. Saying "SQL" without specifying techniques, or "BI tools" without naming Tableau/Power BI/Looker, scores significantly below the ATS threshold for most analyst roles.
Yes — if you use both. They are independent keyword hits. Also list specific Python libraries (pandas, NumPy) and SQL techniques (CTEs, window functions, joins) rather than just the language names.
Name every BI tool you have used: Tableau, Power BI, Looker, Google Data Studio, Metabase, Qlik. Each is a separate ATS keyword — "BI tools experience" scores zero against any of them.
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