Is Data, Analytics & AI a Good Job Market in Charlotte-Concord-Gastonia, NC-SC?

Executive Verdict

Market rating: competitive | Confidence: High

Charlotte is a competitive but worthwhile market for Data, Analytics & AI over the next 3-6 months: metro unemployment was 3.6% in June 2026, metro nonfarm employment was up 1.5743% year over year, and professional and business services employment was up 1.8083% year over year.[12][20][13] Occupation-specific demand is still present: Revelio Public Labor Statistics shows North Carolina Data, Analytics & AI postings up 27.9% year over year in July 2026, and the local sample captured more than 100 postings across more than 75 companies over the last 90 days.[14][31] But it is not an easy market: only about 10% of sampled postings were entry-level, about 55% were mid-level, and only about 5% were remote.[7][8]

Best positioned: Mid-career candidates who can show Python, SQL, and business-facing AI work in banking, consulting, or healthcare have the best odds, because local postings most often ask for python, sql, and generative AI, and the active industry mix leans technology, financial services, consulting, and banking.[32][29]

Main caution: The biggest trap is assuming Charlotte is a remote-first entry market; the sampled mix was about 55% on-site, about 40% hybrid, and only about 5% remote, with entry roles only about 10% of the sample.[8][7]

What Changed Recently

• Revelio Public Labor Statistics shows North Carolina Data, Analytics & AI postings up 27.9% year over year in July 2026 while statewide employment in the occupation family was down 0.8% year over year.[14][15]: That usually means more requisitions are visible, but employers are still choosy about who fills the seats.
• Charlotte's broader economy kept adding jobs in June 2026: total nonfarm employment rose 1.5743% year over year and professional and business services rose 1.8083% year over year, but information employment fell 5.7915% year over year.[20][13][21]: That points to better odds in business-facing analytics teams than in pure tech or media employers.
• The local role mix remains seniority-heavy and office-leaning, with about 55% mid-level roles, about 30% senior roles, about 5% lead+ roles, and only about 5% remote roles in the sample.[7][8]: If you need a first job or a remote-only job, your search window will likely be longer.
• Nationally, the JOLTS job openings rate was 4.4% in June 2026 and openings were up 2.1516% year over year, while the quits rate fell 4.7619% year over year.[22][23]: Open roles still exist, but employers have less urgency to replace people quickly, so interview cycles can drag.

What This Means for You

Entry-Level Candidates

Difficulty: Hard.

Best target: Business-facing analyst roles inside finance, healthcare, and operations teams rather than pure AI-title roles.

Biggest mistake: Applying to ML or AI engineer postings without a portfolio that proves SQL, Python, and stakeholder-facing analysis.

Next step: Build two tight case studies in the next month: one SQL-heavy business analysis and one Python notebook that ends with a one-page recommendation memo for a banking, healthcare, or supply-chain scenario.

Mid-Career Candidates

Difficulty: Moderate, but selective.

Best target: Mid-level analyst, decision science, and analytics consulting roles where you can connect technical work to revenue, risk, cost, or operations decisions.

Biggest mistake: Leading your resume with tools instead of measurable business impact.

Next step: Rewrite your resume around decisions influenced, dollars saved, risk reduced, forecast accuracy improved, or process time cut, then tailor versions for consulting, banking, and enterprise operations.

Career Switchers

Difficulty: Harder than it looks, but possible with domain leverage.

Best target: Adjacent analyst roles tied to your prior industry, such as audit, healthcare operations, or supply chain.

Biggest mistake: Branding yourself as a generalist 'AI professional' without showing domain credibility or real analysis output.

Next step: Pick one domain lane, build a bridge narrative from your past work into analytics, and create one portfolio project that uses the KPIs, language, and business problems of that lane.

Salary Reality

high pay highly concentrated

Official local pay data is uneven by title. BLS shows Charlotte workers overall averaged $69,000 a year in May 2025, while the metro's official data scientist median was $131,110 and local employment in that role was 4,040 in May 2024.[1][2] Directional local pay trackers put Charlotte data-analyst pay around $80,000 to $108,200 on Levels.fyi and $90,704 to $139,537 on Randstad, while the local posting sample centers on about $110k to $150k across the broader Data, Analytics & AI category.[3][4][5]

This is a solid-paying market relative to the local baseline: Revelio Public Labor Statistics shows mean offered salary on new North Carolina Data, Analytics & AI openings at ~$116,501 (n=2,069), versus ~$79,561 across all new openings statewide.[6] In practice, Charlotte pays best when the role is technical, business-critical, or tied to enterprise transformation rather than basic reporting.

The upside comes with selectivity. Only about 10% of sampled postings were entry-level, about 55% were mid-level, and about 30% were senior, so higher pay is tied to experience and scope more than to title alone.[7] Remote choice is also limited, with about 5% of sampled roles advertised as remote.[8]

Best-paying path: Within the evidence here, the strongest pay sits in technical data science and senior enterprise roles: the official local data scientist median was $131,110, the broader local posting sample centers on about $110k to $150k, and Levels.fyi lists Lowe's as the highest-paying local data-analyst employer at $141,000 average total compensation.[2][5][3]

Caution: Do not anchor on a single headline number. Charlotte data-analyst estimates range from $80,000 to $108,200 on Levels.fyi to $90,704 to $139,537 on Randstad, and older local data scientist estimates span $68,461 to $159,408, because these sites mix different seniority levels, titles, and sample methods.[3][4][9]

Where the Opportunities Are Concentrated

Real opportunity is concentrated less in one dominant employer than in a few employer types. The local sample is fragmented across employers, about 40% of postings come from enterprise employers, and the industry mix leans technology (about 30%), financial services (about 20%), IT services and IT consulting (about 15%), professional services and consulting (about 10%), and banking and lending (about 10%).[25][28][29] The most consistently active names in the sample include Deloitte, Synechron, First Citizens, Infosys, Collabera, Matricstek Inc, Kpmg Us, and Infosys Limited.[30] That makes Charlotte a better market for business-facing analytics than for lab-style AI roles. Charlotte-relevant listings included USAA audit analytics and innovation analyst openings plus an Advocate Health data analyst role, and local boards are also surfacing supply-chain data analyst and data scientist openings.[19][18] The market is rewarding people who can apply data work inside finance, healthcare, and operations workflows, not just build models in isolation.

• Financial services and banking analytics (high): Financial services accounts for about 20% of the local posting mix and banking and lending about 10%, and Charlotte-relevant listings included USAA audit analytics openings.[29][19]
• Consulting and enterprise transformation (high): About 40% of sampled postings come from enterprise employers, and active names include Deloitte, Synechron, Infosys, Collabera, and Kpmg Us.[28][30]
• Healthcare operations analytics (moderate): Advocate Health had a Charlotte-relevant data analyst opening, which is a good sign for analysts who can work on operational reporting, access, quality, or cost problems in healthcare settings.[19]
• Supply chain and operations analytics (moderate): Charlotte boards continue to surface supply-chain data analyst and data scientist openings, showing that operations and logistics use cases are still part of the local demand picture.[18]

Where to focus: Aim first at banks, large consultancies, and enterprise operations teams where Charlotte's demand is deepest and where business context is valued alongside technical skill.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python shows up in about 70% of sampled Charlotte postings, making it the clearest screening skill across analyst, data science, and AI-flavored roles.[32]
• SQL (table stakes): SQL appears in about 40% of sampled Charlotte postings, and Charlotte had 207 live SQL jobs with 107 added in the past week, so it remains the cross-role common language for getting screened in.[32][33]
• Generative AI and prompt engineering (differentiator): Generative AI appears in about 20% of sampled local postings and prompt engineering in about 15%, while national guidance treats AI tools and prompt engineering as essential 2026 analyst skills.[32][34]
• Business communication and AI validation (differentiator): National guidance says the role is shifting from query builder to strategic advisor, with more value in framing questions, communicating insights, and validating AI outputs, while governance is becoming the control layer for scaled AI use.[35][36][34]
• AWS and cloud analytics (differentiator): AWS appears in about 20% of sampled local postings, while AWS-certified credentials are explicitly required in less than 5%, which suggests employers care more about working cloud skill than checkbox certification.[32][37]
• dbt, semantic layers, and ELT thinking (premium): Charlotte employers are advertising mid-level data engineer or analyst roles that ask for ETL or ELT pipelines, data models, semantic layers, and BI outputs, and national pay guides place analytics engineering above BI-heavy work.[16][38]
• Cloud AI certification (differentiator): Leading 2026 options include Google Cloud Professional Machine Learning Engineer, Azure AI Engineer Associate, and Databricks Certified Generative AI Engineer Associate; locally, certifications are seldom mandatory, so they work best as proof of depth when you are moving upmarket.[39][37]

Adjacent Roles to Consider

• Data Engineer (pivot): Charlotte employers are advertising mid-level data engineer or analyst roles that call for ETL or ELT pipelines, data models, semantic layers, and BI outputs, so this is a realistic nearby path for technical analysts.[16]
• Supply Chain Analyst (bridge): Local boards are still surfacing supply-chain data analyst and data scientist openings, which signals a practical bridge into operations-focused analytics work.[18]
• Audit or Risk Analyst (bridge): USAA had Charlotte-relevant audit analytics and innovation analyst openings, showing a real bridge between data work and finance-control roles.[19]
• Healthcare Operations Analyst (bridge): Advocate Health had a Charlotte-relevant data analyst opening, which points to crossover demand in care operations and reporting-heavy health systems work.[19]

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into three Charlotte-ready versions: banking and risk, consulting and transformation, and healthcare or operations.
• Build two portfolio pieces that look like local demand: one SQL plus dashboard case and one Python plus business memo case with a clear recommendation.
• Add an 'AI-assisted analysis' section to your portfolio that shows how you used AI tools to speed up work while still validating outputs and explaining tradeoffs.
• Expand your search to hybrid and on-site roles in the metro instead of filtering for remote first.

Days 31-60

• Target one adjacent premium skill stack, such as AWS analytics, dbt and semantic layers, or experiment tracking and model evaluation, and ship one proof project around it.
• Create a one-page deal sheet of quantified wins from past work, with metrics, stakeholders, and decision outcomes, and use it in recruiter screens.
• Build a named-employer target list around banks, consultancies, enterprise operations teams, and healthcare systems, then track referrals and follow-ups weekly.
• If you are underqualified for pure AI titles, shift 30-40% of applications toward audit, operations, supply chain, and domain-specific analyst roles.

Days 61-90

• Decide whether your path is broad analyst, technical data science, or adjacent data-engineering and make your resume, portfolio, and interview stories match only that lane.
• Complete one credential or capstone that proves depth in your chosen lane, but only after your portfolio already shows business impact.
• Run a conversion review of your search by title, sector, and work arrangement, then double down on the combinations that are actually producing interviews.
• If Charlotte direct-hire progress is slow, add contract and consulting channels rather than waiting for a perfect full-time opening.

Methodology and Confidence

This July 2026 report was generated on August 10, 2026. Latest direct national data: August 2026. Latest direct Charlotte-Concord-Gastonia, NC-SC data: July 2026.

Confidence: Overall confidence: High. Charlotte has recent direct local labor data plus fresh local hiring and pay proxies, but some title-level detail is still patchy.

Limitations

• Some Charlotte numbers come from different release schedules, so the broad labor-market context is newer than the most specific role-level wage data.
• This category combines several related job families, so data scientist, data analyst, BI, analytics engineering, and AI-flavored roles do not always move in lockstep.
• Several salary figures here come from employer and compensation trackers rather than official government wage tables, so treat them as directional ranges, not guaranteed offer levels.
• Some occupation-specific direction signals come from statewide data because equivalent Charlotte metro data is not published for that source, so the state trend may not map perfectly to the metro.
• 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 or exact shares.
• Recent BLS year-over-year local changes are preliminary and may be revised in later releases.

References

1. Bureau of Labor Statistics. Occupational Employment and Wages in Charlotte-Concord-Gastonia, NC-SC — May 2025 · 2025-10 · bls.gov
2. Bureau of Labor Statistics. U.S. Bureau of Labor Statistics · 2025-04 · bls.gov
3. Levels. Levels - total_compensation_annual · 2026-08 · levels.fyi
4. Randstadusa. data analyst salaries in charlotte, north carolina | Randstad USA · 2026-08 · randstadusa.com
5. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
6. Reveliolabs. Salaries — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
7. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
8. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
9. Salarymetro. Data Scientist Salary in Charlotte: $107,338 (2024) · 2024-01 · salarymetro.com
10. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-07 · data.bls.gov
11. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-04 · data.bls.gov
12. Stlouisfed. Federal Reserve Bank of St. Louis · 2026-07 · stlouisfed.org
13. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
14. Reveliolabs. Job Openings — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
15. Reveliolabs. Employment — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
16. Salientinsights. Data & AI Salary Guide 2026: What US Employers Are Paying | Salient Insights · 2026-01 · salientinsights.com
17. Builtincharlotte. Best Data Science Jobs in Charlotte, NC 2026 | Built In Charlotte · 2026-08 · builtincharlotte.com
18. Donewithwork. Data Analyst Jobs in North Carolina — 8+ Jobs | DoneWithWork · 2026-08 · donewithwork.com
19. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
21. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
22. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
23. Reveliolabs. Mass-layoff Notices — Revelio Public Labor Statistics (RPLS) · 2026-07 · reveliolabs.com
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31. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
32. Haystackapp. SQL Jobs in Charlotte - 214 Open Positions | Haystack · 2026-08 · haystackapp.io
33. Online. Future of Data Analyst Jobs in an AI-Driven World (2026) · 2026-07 · online.edgewood.edu
34. Improvado. Will AI Replace Data Analysts? 2026 Reality Check · 2026-07 · improvado.io
35. Analytics8. AI and Data Strategy in 2026: What Data Leaders Must Get Right · 2026-06 · analytics8.com
36. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
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38. Online. Will AI Replace Data Analysts? Here · 2026-03 · online.stevens.edu

Live Openings in This Market

This report is published monthly; its companion page tracks the active Data, Analytics & AI openings in Charlotte-Concord-Gastonia, NC-SC from the live Callings.ai job index. Browse current Data, Analytics & AI openings in Charlotte-Concord-Gastonia, NC-SC.