Is Data, Analytics & AI a Good Job Market in Salt Lake City-Murray, UT?

Executive Verdict

Market rating: competitive | Confidence: Medium

Salt Lake City-Murray is a workable but competitive market for Data, Analytics & AI over the next 3-6 months. Local unemployment remains low at 3.4%, and the recent local sample shows more than 50 postings across more than 40 companies over the last 90 days, so jobs do exist.[10][11] But Utah-wide occupation data is essentially flat year over year on employment and up just 0.9% on active postings, while local openings skew mid-level and senior rather than entry-level.[12][13][6] Your best odds are in on-site or hybrid roles tied to education, finance, or tech rather than waiting for a remote-first AI opening.[2][7]

Best positioned: Candidates with 3-7 years of experience in SQL and Python, plus Power BI or Tableau and a clear education, finance, or operations story, have the best odds right now.[2][1]

Main caution: The biggest trap is assuming AI buzz means easy access: only about 5% of local postings are entry-level, only about 10% are remote, and postings that disclose sponsorship show about 0% visa sponsorship.[6][7][14]

What Changed Recently

• Utah's Data, Analytics & AI employment is essentially flat year over year in June 2026, while active postings are up 0.9%.[12][13]: That points to a market with openings but limited net expansion, so many roles are likely backfills or tightly scoped additions.
• Salt Lake City-Murray unemployment was 3.4% in May 2026, below Utah's 3.7% and the latest national 4.3% reading.[10][23][19]: The local economy is still relatively tight, which supports hiring, but it does not remove role-level competition in analytics.
• U.S. job openings rose to 7,594 thousand in May 2026, up 3.8851% year over year, but hires fell to 5,170 thousand, down 2.9655%.[20][21]: Expect more posted jobs than actual seat creation and longer, more selective interview funnels.
• Locally, education accounts for about 35% of the recent hiring mix, and only about 5% of postings are entry-level.[2][6]: A generic portfolio will underperform here; you need a business context and evidence that you can contribute quickly.
• Core analytics still outranks AI hype in local requirements: SQL and Python each appear in about 60% of postings, while machine learning appears in about 25%.[1]: The near-term winner is usually not the most theoretical AI candidate, but the person who can ship analysis and dashboards now and add ML where it helps.

What This Means for You

Entry-Level Candidates

Difficulty: High.

Best target: Business-facing analyst roles in education, public sector, and finance are the most realistic starting lane, because local demand is strongest in education and still meaningful in finance and government/public sector, while only about 5% of postings are entry-level.[2][6]

Biggest mistake: Applying as if this were a remote-first AI market; only about 10% of local postings are remote, and core screens are still SQL, Python, and dashboard tools.[7][1]

Next step: Build one portfolio project in SQL, Python, and Power BI or Tableau around a local-use case such as student outcomes or credit risk, then apply quickly and follow up early because the typical posting stays open around 28 days.[2][1][8]

Mid-Career Candidates

Difficulty: Moderate.

Best target: Mid-level BI, decision support, and applied data science roles are the sweet spot, since about 55% of local postings are mid-level and about 35% are senior.[6]

Biggest mistake: Presenting yourself as a generic data person instead of showing a domain story tied to revenue, risk, operations, or education outcomes.

Next step: Create two resume versions—one for education/public sector and one for finance/tech—and quantify business impact in each.

Career Switchers

Difficulty: High unless you bring strong domain expertise.

Best target: Pivot into analytics inside your current domain, especially education, finance, retail, or operations, rather than trying to jump straight to ML engineer titles.[2]

Biggest mistake: Relying on certificates alone; locally, the most commonly required certifications appear in only about 5% of postings each.[9]

Next step: Translate your prior subject-matter expertise into metrics, dashboards, and SQL-based decision-support examples, then target on-site and hybrid employers first.[7]

Salary Reality

good pay high barrier

The strongest direct local wage anchor is Data Scientists specifically, not the whole category: median pay was $122,370, with a local 25th percentile of $84,700 and 75th percentile of $173,370.[22] For a broader current-market read, local posted salary ranges center on about $94k to $131k, and Utah's mean offered salary on new openings for Data, Analytics & AI was ~$107,935 in June 2026 (n=361).[16][17]

This is strong pay for Utah: the state mean offered salary for Data, Analytics & AI is well above the ~$67,049 mean offered salary across all Utah openings.[17] The catch is that the better-paid slice appears attached to mid-career and specialized work, not broad-access analyst openings.[6]

You are trading pay upside for a narrower funnel: only about 5% of local postings are entry-level, only about 10% are remote, and machine learning appears in about 25% of postings rather than the majority.[6][7][1]

Best-paying path: The strongest pay tends to sit in senior data science and ML-heavy work where Python, SQL, and machine learning are combined.[22][1]

Caution: Do not overread the top end. The $173,370 figure is for Data Scientists specifically and comes from 2024 local wage data, while the local posting band is based on advertised salaries in a partial current sample.[22][16]

Where the Opportunities Are Concentrated

Real opportunity is not evenly spread across the local market. Education accounts for about 35% of recent local Data, Analytics & AI postings, followed by finance and technology at about 15% each, then government/public sector and retail at about 10% each.[2] Named active employers include WGU, Zions Bancorporation NA, Snap Finance, LLC, Filevine, Inc., and RevOps Advisor.[15] That mix points to applied analytics work tied to student outcomes, risk, revenue operations, reporting, and internal decision support more than pure research AI work. Hiring is fragmented across employers rather than dominated by one buyer, which helps diversify opportunity but limits the odds of landing through one mega-employer search.[27] Most openings are mid-level or senior, and the work arrangement mix favors people who can show up in person: about 55% are mid-level, about 35% are senior, about 60% are on-site, and about 35% are hybrid.[6][7] For job seekers, the practical takeaway is to target business-facing analytics roles first. The highest-conversion profile here is someone who can use SQL and Python to answer a domain problem and then present it cleanly in Power BI or Tableau.[1]

• Education and institutional analytics (high): This is the clearest local pocket of demand, with education making up about 35% of the recent hiring mix and WGU appearing among the active employers.[2][15]
• Finance, risk, and revenue analytics (high): Finance represents about 15% of the mix, and named employers include Zions Bancorporation NA, Snap Finance, LLC, and RevOps Advisor, which points to demand around reporting, risk, and operating metrics.[2][15]
• Technology, government, and retail decision support (moderate): Technology accounts for about 15% of the mix, while government/public sector and retail are each about 10%, creating a steady but more specialized lane for BI, analytics, and internal AI use cases.[2]

Where to focus: Prioritize mid-level SQL/Python plus dashboard roles in education and finance that accept on-site or hybrid work, then use pure AI titles as a second search lane.[2][7][1]

Skills and Credentials Worth Pursuing

• SQL (table stakes): SQL shows up in about 60% of local postings, making it the clearest screening skill in this market.[1]
• Python (table stakes): Python also appears in about 60% of local postings, so it is a co-equal baseline with SQL rather than a nice-to-have.[1]
• Power BI or Tableau (differentiator): Power BI and Tableau each appear in about 30% of local postings, which makes dashboard fluency a strong differentiator for business-facing roles.[1]
• Machine learning (premium): Machine learning appears in about 25% of local postings, so it can raise your ceiling, but it is not the main gate for most roles.[1]
• Data visualization (differentiator): Data visualization appears in about 15% of local postings, and it matters more in a market concentrated in education, finance, and operational decision support.[1][2]
• AI-assisted analytics workflow (differentiator): Generative AI is increasingly automating ETL, query writing, and data cleaning, cutting data-preparation time by 50-70%, while most data analyst roles are still expected to use AI rather than be replaced by it.[3][4]
• Azure AI Engineer Associate or Google Cloud Professional Machine Learning Engineer (premium): These certifications are designed around applied AI implementation and cloud-native model deployment, which helps when a role leans beyond classic BI into production AI work.[5]

Adjacent Roles to Consider

• Financial Analyst (both): Finance makes up about 15% of the local mix, and the jump is smaller if you already work with SQL, dashboards, forecasting, or KPI analysis.[2][1]
• Revenue Operations Analyst (bridge): RevOps Advisor is among the active local employers, and revenue operations uses many of the same reporting, funnel-analysis, and stakeholder communication skills as business analytics.[15]
• Institutional Research or Education Analyst (bridge): Education represents about 35% of local demand, making school, university, and student-outcomes analytics a practical nearby lane.[2]
• Risk or Fraud Analyst (both): Local finance hiring and employers such as Zions Bancorporation NA and Snap Finance, LLC suggest a practical adjacent lane for candidates with quantitative and dashboard skills.[15][2]

30 / 60 / 90-Day Plan

First 30 Days

• Rewrite your resume into two versions: one for education/public-sector analytics and one for finance/revenue analytics, because those sectors account for much of the local demand.[2]
• Build one portfolio case study that uses SQL, Python, and either Power BI or Tableau to solve a student-outcomes, credit-risk, or revenue-operations problem.[2][1]
• Expand your search to on-site and hybrid roles in Salt Lake, because about 60% of local postings are on-site and about 35% are hybrid.[7]
• Stop treating remote-only AI titles as your primary plan unless you already have strong ML proof; only about 10% of local postings are remote and only about 25% mention machine learning.[7][1]

Days 31-60

• Publish two targeted portfolio artifacts: one dashboard and one notebook-driven analysis, each with a short business memo explaining the decision you would drive.
• Reach out directly to teams at WGU, Zions Bancorporation NA, Snap Finance, LLC, and Filevine, Inc. with a domain-specific example instead of a generic introduction.[15]
• Add one polished Power BI or Tableau project with executive-level storytelling, since each tool appears in about 30% of local postings.[1]
• If you are moving toward AI work, choose one cloud track—Azure or Google Cloud—and build one small deployed demo before listing the certification path on your resume.[5]

Days 61-90

• Turn your search into a target-employer list and track response rates by sector, title family, and work arrangement so you can double down on what converts.
• If entry-level response is weak, pivot into adjacent titles such as Financial Analyst, Revenue Operations Analyst, or Institutional Research Analyst rather than waiting for a pure junior data role.
• Use interview stories that show business impact, not just tooling, because the local market is concentrated in applied analytics rather than pure research AI.[2]
• Negotiate against the local posted range of about $94k to $131k and Utah's mean offered salary of ~$107,935 for the category, while staying realistic about role scope and seniority.[16][17]

Methodology and Confidence

This June 2026 report was generated on July 10, 2026. Latest direct national data: June 2026. Latest direct Salt Lake City-Murray, UT data: July 2026.

Confidence: Overall confidence: Medium. Local wage, unemployment, and job-shape signals are useful, but some conclusions still rely on statewide occupation trends and a limited local posting sample.

Limitations

• The cleanest metro wage benchmark in this bundle is for Data Scientists specifically, and it is based on May 2024 pay data, so it should be treated as a strong anchor for the higher end of this category rather than the going rate for every analyst or BI role in Salt Lake City-Murray.[22]
• For current hiring direction, some occupation-level signals are only available statewide, so Utah data was used as a proxy for the Salt Lake City-Murray metro when metro-specific occupation series were not published.[12][13]
• The Callings.ai job database is a partial, deduplicated sample of online postings, which makes leading employer names, skill patterns, seniority mix, and job-shape signals more reliable than exact counts or exact local market share.[11][15][2][1]
• Several government year-over-year figures used here are preliminary and may be revised, so short-term changes in unemployment, employment, and national openings should be read as directional rather than final.[23][24][25][18][20][21]
• The local sample shows more than 50 postings over the last 90 days, which is enough to show patterns like the mid-career skew, but still thin for slicing niche sub-roles such as AI engineer, statistician, and analytics engineer separately.[11][6]

References

1. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
2. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
3. Makingscience. The Impact of Generative AI on Data Analytics - Making Science · 2026-01 · makingscience.com
4. Coursera. Will AI Replace Data Analysts? · 2026-06 · coursera.org
5. Ituonline. The Best AI Certifications for IT Professionals in 2026 – ITU Online IT Training · 2026-03 · ituonline.com
6. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
7. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
8. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
9. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
10. Stlouisfed. Federal Reserve Bank of St. Louis · 2026-07 · stlouisfed.org
11. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
12. Reveliolabs. Employment - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
13. Reveliolabs. Job Openings - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
14. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
15. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
16. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai
17. Reveliolabs. Salaries - Revelio Public Labor Statistics (RPLS) · 2026-06 · reveliolabs.com
18. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
19. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-04 · data.bls.gov
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
21. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
22. Onetonline. O*NET OnLine · 2026-05 · onetonline.org
23. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
24. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
25. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-05 · data.bls.gov
26. Reveliolabs. Mass-layoff Notices - Revelio Public Labor Statistics (RPLS) · 2026-04 · reveliolabs.com
27. Callings.ai. Callings.ai job-market aggregation · 2026-06 · callings.ai