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

Executive Verdict

Market rating: competitive | Confidence: Medium

Denver looks active but selective for Data, Analytics & AI over the next 3-6 months. Colorado occupation-level signals show active postings up 13.9% year-over-year even as employment in the field is down 1.2%, which usually means employers are still opening roles but filling them carefully.[14][15] The metro unemployment rate was 3.8% in May 2026, and the local sample still showed more than 175 postings across more than 100 companies over the last 90 days, so this is not a frozen market.[24][16] The main pressure point is level: only about 10% of sampled roles were entry-level, while about 50% were mid-level and about 30% were senior.[3]

Best positioned: The best odds are for a mid-career candidate who can show Python, SQL, and either BI or ML delivery, and who is willing to work on-site or hybrid rather than remote-only.[5][4][3]

Main caution: The biggest mistake is treating Denver as a remote-first junior market; only about 15% of sampled roles were remote and only about 10% were entry-level.[4][3]

What Changed Recently

• Colorado's Data, Analytics & AI openings are up 13.9% year-over-year, while employment in the field is down 1.2% year-over-year.[14][15]: That mix usually means there are live requisitions, but employers are adding seats cautiously and screening harder before they hire.
• Denver's recent sample showed more than 175 postings across more than 100 companies, and the employer mix was fragmented rather than dominated by one company.[16][2]: You have more paths than just a few brand-name employers, but you need a broader target list and faster application cadence.
• National job openings were up 3.8851% year-over-year in May 2026, but hires were down 2.9655% year-over-year.[17][18]: Open roles are real, but conversion from posting to offer is slower, so interview cycles may feel longer than the posting count suggests.
• AI is taking over more of the mechanical analyst workload: roughly 30-40% of traditional analyst tasks are already automated, while the highest-value analyst skills now center on business context translation, stakeholder communication, and model validation.[12][6]: Your portfolio has to show judgment, recommendation quality, and validation of AI-assisted work, not just dashboards or cleaned datasets.
• Local work setup has tilted toward being physically present: about 50% of sampled roles were on-site, about 35% hybrid, and about 15% remote.[4]: A remote-only search cuts you off from most of the market right now.

What This Means for You

Entry-Level Candidates

Difficulty: Harder than average because only about 10% of sampled roles were entry-level, and the common baseline skills were Python and SQL rather than just spreadsheet comfort.[3][5]

Best target: Aim for analyst and BI-heavy roles inside healthcare, public-sector, financial, or operations teams where SQL, Tableau, and data storytelling matter more than deep research-grade ML.[8][5]

Biggest mistake: Leading with certificates alone and no proof that you can answer a business question with real data.

Next step: Build two portfolio pieces fast: one SQL plus dashboard case and one Python analysis with a written recommendation, then apply broadly to on-site and hybrid roles instead of waiting for remote entry openings.[4][5]

Mid-Career Candidates

Difficulty: Moderate. The market skews toward mid-level and senior hiring, with about 50% of sampled roles at mid level and about 30% at senior.[3]

Best target: Target teams that need delivery plus stakeholder fluency, especially in technology, government, healthcare, aerospace and defense, and financial services.[8][6]

Biggest mistake: Applying with a generic resume that lists tools but does not show ownership of decisions, metrics, or production outcomes.

Next step: Create role-specific versions of your resume for BI/analytics and DS/ML tracks, and show where you used Python, SQL, machine learning, Tableau, or PyTorch to change an operating decision.[5]

Career Switchers

Difficulty: Moderate to hard because most postings that state education requirements still lean toward a bachelor's degree and the market is not especially junior-friendly.[13][3]

Best target: Use bridge roles first: reporting, revenue operations, finance analytics support, or business operations work that lets you prove SQL, visualization, and stakeholder communication before chasing pure data scientist titles.[5][6]

Biggest mistake: Rebranding into AI titles before you can demonstrate core analytics execution and business context.

Next step: Position your prior domain knowledge as the differentiator, then add one AI-assisted workflow example that shows you can use automation without losing accuracy or business judgment.[6][7]

Salary Reality

high pay highly concentrated

Observed local government pay data is broad rather than title-perfect: in May 2024, Denver-area STEM wages were $30.59 at the 25th percentile, $40.08 at the median, and $56.39 at the 75th percentile.[25] More current directional signals put local posted salary ranges for Data, Analytics & AI around about $108k to $160k, with a broader about $91k to $215k band, while Revelio Public Labor Statistics shows a Colorado mean offered salary of about $121,568 on new openings in June 2026, based on n=1,722.[26][27]

This is clearly a good-paying field in Colorado: the statewide mean offered salary for Data, Analytics & AI openings was about $121,568 versus about $81,062 across all occupations.[27]

The money is offset by selectivity. Local openings skew mid and senior, only about 15% of the sample was remote, and the stronger salary bands usually come with specialization rather than broad-access hiring.[4][3][26]

Best-paying path: The strongest pay tends to sit in specialized Python-heavy ML and higher-end analytics work, especially in consulting, defense-linked, and enterprise settings where active employers included CACI, Deloitte, Kpmg Us, Kpmg Llp, Slalom, LLC, and Grey Matters Defense Solutions, LLC.[1][11][5]

Caution: Do not overread the top of the local salary band. Posted ranges are not the same as accepted pay, and they reflect a partial sample that includes senior and niche roles more heavily than entry roles.[26][3]

Where the Opportunities Are Concentrated

Opportunity is spread across a long tail rather than locked inside one dominant employer. Over the last 90 days, the local sample showed more than 175 postings across more than 100 companies, and hiring was described as fragmented across employers; the most consistently active names included CACI, Deloitte, Migrate Mate, Kpmg Us, RevOps Advisor, Kpmg Llp, Slalom, LLC, and Grey Matters Defense Solutions, LLC.[16][2][1] That matters because the best search strategy here is broad targeting by problem type and employer type, not waiting on a few famous logos. The work is also spread across multiple end markets. In the local sample, technology accounted for about 40% of postings, government & public sector about 15%, healthcare about 10%, aerospace & defense about 10%, and financial services about 10%.[8] About 30% of postings came from enterprise employers, which helps candidates who know how to work with layered stakeholders, compliance, and long decision cycles.[11] The real bottleneck is fit, not total market collapse. About 50% of sampled roles were on-site, about 35% hybrid, and about 15% remote, while about 50% were mid-level and only about 10% were entry-level.[4][3] If you are junior or remote-only, the market feels much smaller than the headline activity suggests.

• Technology product and platform-adjacent analytics teams (high): Technology made up about 40% of the local sample, and the common tool stack centered on Python, SQL, machine learning, Tableau, and data visualization.[8][5]
• Government, public sector, and defense analytics (moderate): Government & public sector accounted for about 15% of local postings and aerospace & defense about 10%, with employers such as CACI and Grey Matters Defense Solutions, LLC appearing among the active names.[8][1]
• Healthcare and financial-services analytics (moderate): Healthcare and financial services each represented about 10% of local postings, which favors candidates who can connect analysis to operations, compliance, revenue, or service delivery rather than only model building.[8]
• Remote-only junior generalist roles (limited): This is the tightest part of the market because only about 15% of sampled roles were remote and only about 10% were entry-level.[4][3]

Where to focus: Focus on mid-level, Python-and-SQL-heavy roles in technology, consulting, public-sector, defense, healthcare, and financial-services teams, and widen your search to on-site and hybrid openings.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appeared in about 70% of sampled local postings, making it the clearest baseline skill across analyst, data science, and AI-leaning roles.[5]
• SQL (table stakes): SQL showed up in about 40% of local postings and remains the common denominator for analyst, BI, and operations-facing work.[5]
• Machine learning and PyTorch (premium): Machine learning and PyTorch each appeared in about 20% of local postings, so they are not universal requirements but they clearly separate ML-track candidates from generalist analysts.[5]
• Tableau and data visualization (differentiator): Tableau, data analysis, and data visualization each appeared in about 20% of local postings, which shows that insight delivery still matters even as more analysis is AI-assisted.[5][6]
• Business context translation, stakeholder communication, and model validation (premium): 2026 analyst guidance points to business context translation, stakeholder communication, and model validation as the highest-value skills, ahead of raw dashboard production alone.[6]
• Prompt engineering and AI workflow automation (differentiator): Prompt engineering and AI workflow automation are flagged as essential AI skills for 2026, which fits a market where routine analyst tasks are increasingly automated.[7][12]
• Microsoft Azure certification (differentiator): Microsoft Azure was the most commonly cited certification locally, but it appeared in less than 5% of sampled postings, so it is more of a tie-breaker than a gatekeeper.[9]
• MLOps, Generative AI, and Databricks Certified Machine Learning Professional (premium): Staying current with MLOps and Generative AI is increasingly important for data scientists in 2026, and the Databricks Certified Machine Learning Professional is a recognized applied credential for that path.[19][10]

Adjacent Roles to Consider

• Revenue Operations Analyst (bridge): It reuses SQL, reporting, dashboarding, and stakeholder-management skills, but moves you closer to sales and go-to-market operations.
• FP&A Analyst (pivot): It rewards the same pattern-finding and decision-support strengths, especially for candidates who can explain numbers to leaders.
• Business Operations Analyst (both): This path uses analytics to improve process, cost, service, and operating metrics without requiring a pure data title.
• Product Operations Manager (pivot): It fits candidates who are good at instrumentation, reporting, experiment readouts, and turning messy signals into product decisions.
• Risk Analyst (bridge): It keeps you close to quantitative decision-making while giving you a more specialized business lane in finance, insurance, or compliance-heavy settings.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two tracks: an analyst/BI version built around SQL, Tableau, data visualization, and business recommendations, and a DS/ML version built around Python, machine learning, and PyTorch.[5]
• Build a target list around the long tail of active local employers instead of just big-tech names; recent active employers included CACI, Deloitte, Migrate Mate, Kpmg Us, RevOps Advisor, Kpmg Llp, Slalom, LLC, and Grey Matters Defense Solutions, LLC.[1]
• Rework your search filters to include on-site and hybrid openings because about 85% of the local sample was not remote.[4]
• Publish one portfolio case that shows business context translation and one that shows AI-assisted analysis with human validation, because those are the skills moving up in value.[6][7]

Days 31-60

• Create sector-specific applications for technology, public-sector, healthcare, aerospace and defense, and financial-services employers because those were the most active local industry buckets.[8]
• If you are analyst-first, add stronger Python proof; if you are DS-first, add clearer SQL and visualization proof, since the local market still rewards both foundations.[5]
• Practice concise case storytelling: problem, data source, method, recommendation, and business outcome, not just notebook screenshots or dashboard tours.[6]
• For enterprise and consulting paths, add one cloud or platform signal such as Azure or Databricks only after the core portfolio is solid, since local certification demand was limited and selective rather than universal.[9][10]

Days 61-90

• Broaden into adjacent openings if conversions are weak, especially revenue operations, FP&A, business operations, product operations, or risk roles that still reward analytics strengths.
• Start a local-intensity outreach rhythm with recruiters and hiring managers at consulting, defense, and enterprise employers, where the market has both active names and a meaningful enterprise share.[1][11]
• Replace any generic 'AI enthusiast' branding with concrete artifacts: a prompt workflow, an automated reporting step, and a model-validation example that shows judgment over automation.[12][7][6]
• If your interviews are stalling, run a positioning reset: choose one lane—BI/analytics, decision science, or DS/ML—and make every project, keyword set, and interview story reinforce that lane.

Methodology and Confidence

This June 2026 report was generated on July 10, 2026. Latest direct national data: June 2026. Latest direct Denver-Aurora-Centennial, CO data: July 2026.

Confidence: Overall confidence: Medium. The report has solid local context and salary signals, but some conclusions still rely on broader category and state-level inference.

Limitations

• The newest hard local labor context is current, but the clearest official local occupation count and wage anchor for this category is older and does not capture every shift from the last year.
• This category bundles several different jobs, from analyst and BI work to data science and ML-oriented roles, so competition and pay can vary a lot inside the same metro.
• Statewide occupation trend data was used as a proxy for Denver when equivalent metro-level trend data was not available, so the hiring direction is better read as Colorado-plus-Denver context rather than a precise metro headcount change.
• Several year-over-year government comparisons in this report are preliminary, which means the direction is useful but small percentage changes can still be revised.
• The Callings.ai job database is a partial, deduplicated sample of online postings, so direction of demand, leading employer names, and skill patterns are more reliable than exact counts, exact shares, or the true size of the market.

References

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