Is Data, Analytics & AI a Good Job Market in Denver-Aurora-Centennial, CO?

Executive Verdict

Market rating: competitive | Confidence: High

Denver is a competitive market for Data, Analytics & AI, not a collapsing one. Metro unemployment was 3.8% seasonally adjusted in January 2026, and the broader local unemployment rate was 4.2%, down year over year, but metro employment and labor force were also lower than a year earlier, which argues for caution before calling this a broad expansion.[2][12][13][14] In the local hiring sample, we observed more than 50 postings across more than 50 companies with no clear directional trend, and the mix skewed heavily senior rather than entry-level.[15][16] That leaves real opportunity for experienced, specialized candidates, but a much harder path for juniors and remote-only applicants.[17][11]

Best positioned: Experienced candidates who can show SQL and Python fluency, strong data modeling, and a clear domain story in IT, healthcare tech, or defense-adjacent work have the best odds.[18][16][19]

Main caution: The biggest mistake is treating Denver's low unemployment as proof that junior data hiring is broad; only about 10% of sampled openings were entry level and only about 20% were remote.[2][16][17]

What Changed Recently

• Denver's unemployment rate improved to 4.2% in January 2026 and was down 14.3% year over year, but employment and labor force were also down -0.8% and -1.5%.[12][13][14]: The market looks healthier on the surface, but some of that improvement reflects a smaller or softer labor pool rather than obvious broad-based job growth.
• Local Information employment fell 4.0% year over year and Financial Activities fell 2.1%, while Education and Health Services grew 4.9% in January 2026.[8][9][20]: For Denver data job seekers, that shifts the near-term odds toward healthcare and operational analytics rather than assuming every tech or finance employer is adding headcount.
• The visible local job sample showed more than 50 postings across more than 50 companies, with no clear directional trend, fragmented employer demand, and a senior-heavy mix.[15][10][16]: You should expect a wider search across employer types and more competition for each opening, especially below senior level.
• National CPI was up +3.3% year over year in March 2026, average hourly earnings were up +3.5%, and the federal funds rate stood at 3.64%.[3][4][5]: That combination points to slow real wage improvement and continued budget discipline, so Denver candidates need sharper business cases and tighter salary negotiation.

What This Means for You

Entry-Level Candidates

Difficulty: Hard. The sample skews to about 10% entry-level openings while about 60% are senior, so you are competing for a thin junior slice.[16]

Best target: Aim at BI analyst, junior analytics engineer, healthcare analytics, and reporting-heavy roles that showcase SQL, Python, visualization, and data analysis rather than pure ML-first titles.[20][18][19]

Biggest mistake: Applying only to remote data scientist roles is the fastest way to stall; only about 20% of sampled openings were remote, and the typical active posting had been open around 54 days.[17][11]

Next step: Build one portfolio package around a local industry problem such as healthcare operations, telecom customer analytics, or finance reporting, with a SQL project, a Python workflow, and a dashboard artifact mapped to one job family.[18][19]

Mid-Career Candidates

Difficulty: Moderate. There is real demand, but the market rewards specialization more than generalist analytics, and more than 50 recent postings were spread across more than 50 companies rather than concentrated in a few easy targets.[15][10][27]

Best target: Go after senior analyst, analytics engineer, decision scientist, data engineer, and applied ML roles in information technology, healthcare technology, and defense-related employers.[18][16]

Biggest mistake: Leading with tools instead of business outcomes. Hiring is increasingly driven by specialization in emerging technologies and domain expertise rather than general skills.[27]

Next step: Rework your resume into two versions: one for analytics engineering and data platform work centered on SQL, Python, and data modeling, and one for decision-support work centered on experimentation, visualization, and stakeholder impact.[19]

Career Switchers

Difficulty: Hard but possible if you narrow the story. Denver hiring is fragmented and senior-heavy, so broad 'I can do anything with data' positioning usually loses to candidates with a cleaner domain fit.[10][16]

Best target: Switch through domain-adjacent analytics roles in healthcare, finance, telecom, real estate services, or compliance-heavy teams rather than jumping straight to ML engineer or AI engineer titles.[18][9][20]

Biggest mistake: Relying on certificates alone. Among postings that state an education requirement, bachelor's and master's degrees dominate, and only a small share explicitly require a certification.[31][29]

Next step: Use your prior industry as the wedge, then add a beginner certificate such as the Google Data Analytics Professional Certificate or the IBM Data Analyst Professional Certificate only if it fills an obvious basics gap.[32]

Salary Reality

high pay highly concentrated

Observed local government pay is solid but broad-brush: computer and mathematical occupations averaged $60.06 an hour in May 2024, while business and financial operations averaged $102,010 annually.[21] More current posted salaries in the local job sample center on about $122k to $160k, with a broader 25th-75th band of about $96k to $200k, but those posting-based figures are directional rather than a full market average.[22]

Denver can pay very well for experienced data talent, especially where engineering, analytics, and domain knowledge overlap. The gap between older BLS wage groups and current posting ranges suggests the strongest offers cluster in higher-seniority and more technical roles, not across the whole category.[21][22][16]

The upside comes with real filters: about 60% of sampled openings are senior, only about 20% are remote, and employers most often ask for SQL and Python before they ask for more specialized tools.[16][17][19]

Best-paying path: The best-paying path appears to sit around data engineering and AI-leaning roles. Colorado's Q4 2025 report listed Data Engineer among top-paying roles at $148,502, and national guides put AI engineer and AI product manager pay well above general analyst roles.[23][24][25]

Caution: Do not overread the top end. Current pay signals mix government wage surveys, Colorado or metro proxies, national salary guides, and posted ranges, and those measures can diverge sharply by seniority, sub-role, and compensation definition.[21][23][25][24][22]

Where the Opportunities Are Concentrated

Real opportunity is spread across several employer types rather than one dominant cluster. In the local job sample, hiring was fragmented across employers, with more than 50 postings across more than 50 companies, and the most-active industries were information technology (about 35%), technology (about 20%), healthcare technology (about 10%), real estate services (about 5%), and defense and space (about 5%).[15][10][18] That mix matters because the underlying metro economy is not moving uniformly. Local Information employment was 45.7 thousand in January 2026 and down 4.0% year over year, Financial Activities was 114.0 thousand and down 2.1%, and Professional and Business Services was 311.4 thousand and roughly flat at -0.1%, while Education and Health Services was 226.1 thousand and up 4.9%.[8][9][33][20] For job seekers, that points toward cross-functional roles that help operating teams make decisions, especially in healthcare tech and durable enterprise environments, rather than betting only on consumer-tech or pure research openings.

• IT and platform data roles (high): The largest visible slice of openings sits in information technology and technology, together about 55% of the sampled category, favoring analytics engineering, BI, data platform, and applied ML work.[18]
• Healthcare tech and care operations analytics (high): Healthcare technology is about 10% of the posting mix, and local Education and Health Services employment was up 4.9% year over year, giving this segment better operating momentum than several other white-collar sectors.[18][20]
• Finance, risk, and revenue analytics (moderate): Financial Activities still matters because Denver has 114.0 thousand local jobs in the sector, but January 2026 employment was down 2.1% year over year, so expect selectivity and heavier emphasis on efficiency, risk, and revenue analytics.[9]
• Defense and space data work (moderate): Defense and space is only about 5% of the posting mix, but it can reward strong technical depth for decision science, data engineering, and operations research work.[18]

Where to focus: If you need traction fast, target SQL/Python-heavy roles inside healthcare tech and enterprise platform teams, then treat pure AI-brand titles as a second pass.

Skills and Credentials Worth Pursuing

• SQL (table stakes): SQL appears in about 55% of sampled postings, making it the clearest common denominator across analyst, BI, data engineering, and decision-support roles.[19]
• Python (table stakes): Python also appears in about 55% of sampled postings, so it is now baseline rather than premium for many Denver data roles.[19]
• Data modeling (differentiator): Data modeling shows up in about 15% of sampled postings and is one of the clearest signals that a candidate can move beyond analysis into durable data infrastructure work.[19]
• Data visualization and Tableau (table stakes): Data visualization and Tableau each appear in about 15% of sampled postings, which makes them important for analyst, BI, and stakeholder-facing roles even in a more technical market.[19]
• Machine learning (differentiator): Machine learning appears in about 15% of sampled postings, so it helps most when paired with strong SQL, Python, and domain knowledge rather than as a standalone identity.[19][27]
• Domain expertise in healthcare or regulated operations (premium): Hiring is increasingly driven by specialization and domain expertise, and Denver's visible demand clusters include healthcare technology, finance-adjacent work, and defense-related employers.[27][18]
• MLOps and AI deployment (premium): Production-ready AI work increasingly expects MLOps and deployment discipline rather than notebook-only skills.[28]
• Certified Data Engineer (differentiator): It is the certification most often named in sampled postings, although only about 5% of postings explicitly require a certification, so it helps more as a tie-breaker than a gatekeeper.[29]

Adjacent Roles to Consider

• Data Engineer (both): It uses much of the same SQL, Python, and data modeling base but puts more weight on pipelines and platform reliability.[19]
• Analytics Engineer / BI Engineer (bridge): This is a realistic bridge for candidates who already translate business questions into dashboards, metrics, and warehouse logic.
• Healthcare Analytics / Population Health Analyst (both): Denver shows both healthcare technology demand and stronger local momentum in Education and Health Services than in Information or Financial Activities.[18][20][8][9]
• AI Product Manager (pivot): For senior ICs who already influence roadmap decisions, it is a credible pivot that keeps one foot in analytics and one in strategy.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two clear lanes: analytics/BI and data platform/engineering. Do not send one blended document to every role.
• Build one Denver-relevant case study tied to a local demand pocket such as healthcare operations, telecom customer analytics, or finance reporting.
• Rewrite your headline and summary around outcomes, not tools. Lead with decisions improved, revenue protected, costs reduced, or cycle time cut.
• Audit every saved job against four must-haves: SQL, Python, domain fit, and work arrangement. Drop roles where you miss three of the four.

Days 31-60

• Publish a compact portfolio that includes one SQL project, one Python workflow, and one stakeholder-facing dashboard or memo.
• Create a target list of Denver employers across IT, healthcare tech, defense, and enterprise services rather than waiting on a few marquee companies.
• Add one production-minded skill that moves you upmarket, such as dbt-style modeling, orchestration, testing, or model deployment.
• Practice interview stories that connect analysis to business action, especially tradeoffs, stakeholder pushback, and decision impact.

Days 61-90

• If traction is weak, pivot your search toward adjacent roles like analytics engineer, data engineer, healthcare analytics, or AI product support instead of repeating the same title search.
• Use salary conversations to anchor on scope and seniority first, then negotiate for total compensation, flexibility, and growth path rather than chasing the highest headline number.
• If you are a switcher, finish one recognized starter credential only after your portfolio and positioning are already coherent.
• Measure your funnel by role family and industry. Double down on the combinations that return interviews, and cut the ones that only return automated rejections.

Methodology and Confidence

This March 2026 report was generated on April 22, 2026. Latest direct national data: April 2026. Latest direct Denver-Aurora-Centennial, CO data: April 2026.

Confidence: Overall confidence: High. The report leans on recent local labor-market readings and current local hiring patterns, while treating salary guides and posting-based measures as support rather than the core signal.

Limitations

• The freshest local context is from January 2026, but the strongest local government wage benchmark for broad computer and mathematical occupations is from May 2024, so current pay conditions may be somewhat different from the last official wage release.[21]
• This category spans analysts, BI specialists, data engineers, statisticians, ML engineers, and AI engineers, so no single wage or hiring signal should be read as a perfect proxy for every sub-role.
• Several January 2026 year-over-year changes in Denver unemployment, employment, and labor-force data are preliminary and may be revised, so small movements should be read as directional rather than final.[12][13][14]
• The Callings.ai job database used for hiring volume, employer names, salary bands, seniority mix, and skills is a partial, deduplicated sample of online postings, so direction of demand, leading employer names, and recurring skill patterns are more reliable than exact counts or exact shares.[15][26][22][16][19]
• Current pay signals mix government wage surveys, Colorado or metro proxy figures, national salary guides, and posted salary ranges, which can disagree because they measure different geographies, seniority levels, and compensation definitions.[21][23][25][24][22]

References

1. Federal Reserve Economic Data. Unemployment Rate · 2026-03 · fred.stlouisfed.org
2. Federal Reserve Economic Data. Unemployment Rate in Denver-Aurora-Lakewood, CO (MSA) · 2026-04 · fred.stlouisfed.org
3. Federal Reserve Economic Data. Consumer Price Index for All Urban Consumers: All Items in U.S. City Average · 2026-03 · fred.stlouisfed.org
4. Federal Reserve Economic Data. Average Hourly Earnings of All Employees, Total Private · 2026-03 · fred.stlouisfed.org
5. Federal Reserve Economic Data. Federal Funds Effective Rate · 2026-03 · fred.stlouisfed.org
6. Federal Reserve Economic Data. All Employees, Total Nonfarm · 2026-03 · fred.stlouisfed.org
7. Threads. WhatLayoff (@whatlayoff) on Threads · 2026-01 · threads.com
8. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov
9. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov
10. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
11. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
12. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov
13. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov
14. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov
15. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
16. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
17. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
18. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
19. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov
21. Bureau of Labor Statistics. Occupational Employment and Wages in Denver-Aurora-Centennial — May 2024 · 2025-08 · bls.gov
22. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
23. Cochamber. Cochamber - median_wage_annual · 2025-12 · cochamber.com
24. Lorienglobal. 2026 Tech Salary Guide | Salary Insights for 150+ Roles in The US | Lorien · 2026-01 · lorienglobal.com
25. Aquent. Aquent's 2026 Salary Guide Proves That AI is Changing Work | Aquent · 2026-01 · aquent.com
26. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
27. Forwrd. Data Science Hiring Trends Report: 2025 Outlook | Forwrd.ai · 2025-01 · forwrd.ai
28. Refontelearning. Refonte Learning : Data Science & AI in 2026: Top Trends, Essential Skills, and Career Strategies · 2026-02 · refontelearning.com
29. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
30. Montecarlodata. Future Of Data Analytics: 10 Trends To Watch Out For In 2026 · 2025-12 · montecarlodata.com
31. Callings.ai. Callings.ai job-market aggregation · 2026-03 · callings.ai
32. Dataquest. 12 Best Data Analytics Certifications in 2026 · 2025-12 · dataquest.io
33. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-01 · data.bls.gov