Get your Data Analyst resume past ATS.

According to Coursera's Workforce Analytics data, over 60% of data analytics job postings list SQL as one of the top three must-have skills, and the demand for SQL-skilled data professionals grew 40% between 2022 and 2025. The BLS projects data professional employment will grow 34% from 2024 to 2034 — far above the 4% average across all occupations — creating fierce applicant volume at every level (BLS OOH, 2024). High applicant volume means ATS threshold filters are set tighter, not looser.

• "SQL queries (window functions, CTEs, multi-table joins)" — parenthetical specificity signals advanced SQL fluency. ATS models for mid-to-senior analyst roles frequently weight SQL sub-skill terms beyond the top-level keyword.
• "50M-row PostgreSQL database" — data scale plus specific database name. "PostgreSQL" and "MySQL" and "BigQuery" are separate ATS keyword targets from "SQL" itself; name the database system.
• "reduced weekly data pull time from 6 hours to 20 minutes" — time-savings metric. Efficiency gains are the most commonly missing metric type on analyst resumes and carry high ATS scoring weight.
• "$340M in annual transaction volume" — business context. Scope of the data you work with signals company size and analytical complexity.

Verdict: Do write at least one SQL bullet that names the specific database (PostgreSQL, MySQL, Snowflake, BigQuery, Redshift), the query type (window functions, CTEs, stored procedures), the data scale (row count or GB), and the business outcome. Don't list "SQL" in a comma-separated skills line and leave it there — it is a threshold keyword that needs evidence bullets to differentiate your application.

As of 2025, Power BI holds approximately 13.5% of the BI market and Tableau approximately 12.9%, making them the two largest players by customer count (Power BI: ~114,000 customers; Tableau: ~87,000) according to market research compiled by Intuition Labs BI Platform Analysis, 2024. But the distribution is not uniform: Power BI dominates at Microsoft-stack enterprises (Office 365, Azure) while Tableau leads at data-mature tech companies, large analytics teams, and organizations using Salesforce (Tableau is a Salesforce product post-2019 acquisition).

Verdict: Do check the job description's tech stack (especially whether the company runs Azure, Google Cloud, or Salesforce) before choosing which BI tool to lead with. Don't list "Tableau, Power BI, Looker" as a flat comma list — ATS and hiring managers both value depth over breadth; one tool named with specific feature vocabulary outscores three tools named with no context.

The BLS Occupational Outlook reports the median wage for Operations Research Analysts (a category that overlaps heavily with advanced data analyst roles) was $91,290 in May 2024, while Market Research Analysts and Marketing Specialists reported a median of $76,950 (BLS OOH, 2024). That $14,000 median gap reflects genuine functional differences — and those functional differences map directly to separate ATS keyword models.

Verdict: Do identify which analyst archetype the job description targets before tailoring your resume. The fastest signal is the primary output described: "decision-support dashboards" = BA; "experiment design" = Product; "channel ROI" = Marketing; "financial model" = Finance. Match your keyword layer to that archetype. Don't submit a single generic data analyst resume across all four types — it will score below threshold for each.

Most analyst resumes describe what the analysis produced but not what it operated on. "Analyzed customer data" tells an ATS nothing it can score. "Analyzed 15 million customer records across 3 years of transaction history" gives the ATS a parseable scale signal that elevates the match score for mid-to-senior postings that filter on data complexity.

The BLS projects about 23,400 annual openings for data scientists through 2034 at a 34% growth rate, and the adjacent data analyst category follows a similar expansion curve (BLS OOH, 2024). Growing applicant pools at every tier mean ATS filters are being calibrated tighter. Scale vocabulary is the lowest-effort, highest-impact addition most analyst resumes can make.

Verdict: Do add a data scale qualifier to every significant analysis bullet: row count, GB/TB volume, or record count. "50M-row," "2TB Snowflake warehouse," or "120M transaction records" are all parseable ATS signals. Don't describe data work without scale — it reads as junior-tier regardless of the actual complexity involved.

The most common analyst resume pattern is a list of tools and deliverables with no outcome: "Built Tableau dashboards for the sales team." Every claim of that type scores near zero on an ATS trained on outcome-dense job descriptions. The fix is a three-part structure: data source and scale, analytical method, business outcome.

• "7 Tableau dashboards" — count matters. "Suite of dashboards" is not ATS-scoreable; "7 dashboards" is.
• "22M-row Snowflake data warehouse" — tool name + scale. Both Snowflake and the row count are separate ATS keyword targets.
• "SQL CTEs and window functions" — SQL sub-skills beyond the table-stakes keyword level. Differentiates mid-level from junior on SQL-specific ATS filters.
• "Sales, Finance, and Customer Success" — cross-functional scope. Signals stakeholder breadth that ATS models for mid-to-senior analyst roles explicitly weight.
• "14 hours/week" — efficiency metric. Time-savings are among the most commonly missing and most highly weighted metric types on analyst resumes.
• "$90M ARR pipeline" — business context scope. Anchors the analytical work to a revenue figure the ATS can parse as a business-impact signal.

Verdict: Do give every dashboard or reporting bullet three elements: the BI tool + data source + scale, the analytical method, and the business outcome in time or dollars. Don't describe what you built without explaining what changed because of it — ATS systems score outcomes, not activities.