Is Data, Analytics & AI a Good Job Market in Dallas-Fort Worth-Arlington, TX?

Executive Verdict

Market rating: competitive | Confidence: High

Dallas-Fort Worth is a real market for Data, Analytics & AI, with more than 300 recent postings spread across more than 200 companies, and the metro's professional and business services base grew 3.1429% year-over-year in August 2026.[7][18] But it is a competitive market rather than an easy one: metro unemployment was 4.4% in August 2026, local information employment was down 2.0690% year-over-year, and the posting mix skewed toward mid and senior talent.[33][13][22] The near-term read is best for experienced candidates who can work on-site or hybrid and show production-grade Python, SQL, and AI workflow skills.[23][9]

Best positioned: The best odds right now belong to a mid-career analyst or data scientist who can show Python and SQL fluency, some machine-learning or generative-AI workflow experience, and openness to hybrid or on-site roles.[9][23]

Main caution: Do not confuse AI excitement with easy access: only about 15% of sampled openings were entry-level and only about 5% were remote.[22][23]

What Changed Recently

• Texas Data, Analytics & AI postings were up 41.9% year-over-year in September 2026 even though statewide employment in the field was down 1.3%.[6][5]: There are more openings to chase, but this looks more like selective backfilling and project hiring than broad-based easy expansion.
• Dallas-Fort Worth professional and business services employment rose 3.1429% year-over-year in August 2026, while local information employment fell 2.0690%.[18][13]: Analytics demand is looking healthier in consulting and enterprise services than in pure tech-heavy employer pockets.
• The local posting mix stayed experience-heavy, with about 35% mid-level, about 35% senior, about 15% lead+, and only about 15% entry-level openings; remote roles were only about 5% of the sample.[22][23]: Most candidates will need a sharper specialization story than they did in looser hiring cycles.
• National inflation and wage growth were both running around 3.0% year-over-year, while the federal funds rate sat at 3.75% in September 2026.[19][20][21]: Employers may have a bit more budget room than last year, but candidates should not assume big real-pay jumps without a strong niche.

What This Means for You

Entry-Level Candidates

Difficulty: Hard. Only about 15% of sampled openings were entry-level, and more than one-third of entry-level jobs nationally now ask for some AI competency.[22][31]

Best target: Aim first at analyst and BI-style roles in consulting, financial services, and retail where SQL, Python, Tableau, and data visualization show up repeatedly in postings.[24][9]

Biggest mistake: Applying as a generic AI enthusiast without proof of SQL, dashboarding, and a business use case is the fastest way to blend in.[9]

Next step: Build two portfolio pieces in the next month: one SQL/Tableau business dashboard and one Python notebook that uses a generative-AI or RAG workflow with clear validation notes.[9][25]

Mid-Career Candidates

Difficulty: Moderate. The sample skews toward mid and senior talent, with about 35% mid-level and about 35% senior openings.[22]

Best target: Target enterprise analytics teams inside IT services, financial services, business consulting, and retail, where the local posting mix is deepest.[24]

Biggest mistake: Leading with tools only instead of showing how your analysis changed pricing, operations, risk, or customer decisions.

Next step: Rewrite your resume around three quantified wins and add one case study that shows Python, SQL, and either machine learning or generative-AI workflow design in a business setting.[9]

Career Switchers

Difficulty: Hard but possible if you bring domain credibility. Among postings that state an education requirement, bachelor's degrees show up about 70% of the time and master's degrees about 20% of the time.[32]

Best target: Move into domain-heavy analytics roles next to your prior industry, such as finance, retail, healthcare operations, or consulting, rather than jumping straight to pure research or platform-heavy ML work.

Biggest mistake: Trying to out-compete full-time data scientists on technical depth before you have a domain story that employers can monetize.

Next step: Package your prior work into metrics problems like forecasting, reporting, segmentation, or process optimization, and add a lightweight Python and SQL portfolio that proves you can operate in current workflows.[9]

Salary Reality

high pay highly concentrated

The clearest local government pay benchmark is for data scientists: Dallas-Fort Worth had 10,120 data scientist jobs in 2025, with mean pay of $123,150, median pay of $127,750, a 25th percentile of $90,510, and a 75th percentile of $146,750.[4][1][28][2][29] Broader local posting-based signals are a little higher at the center but much noisier: sampled posted salary ranges for Data, Analytics & AI cluster around about $107k to $166k, while Salient Insights showed Data Analyst postings around $90,000–$105,000 and Data Scientist postings around $115,300–$170,900 as of September 28, 2026.[30][10]

This is a strong-paying market relative to the metro-wide wage base: the broad computer-and-mathematical group averaged $57.55 per hour in Dallas-Fort Worth versus $33.96 across all occupations locally.[3] The premium is real, but it is not spread evenly across every analytics title.

The salary upside is offset by tighter screening, limited junior inventory, and much less remote flexibility than many candidates expect.[22][23]

Best-paying path: The strongest pay tends to sit in data scientist and AI-heavy roles that combine Python, machine learning, generative AI, and business domain ownership.[10][9]

Caution: Do not overread the top end: the 90th-percentile local data scientist wage reflects an established occupation, while some live posting samples are tiny—seven salary-disclosing Data Analyst postings, eight Data Scientist postings, and only one disclosed-salary Analytics Engineer posting in Salient's local cut.[27][10]

Where the Opportunities Are Concentrated

Most real opportunity is in enterprise analytics, not just startup-style AI hiring. In the local posting mix, IT services and consulting accounts for about 30%, software development about 15%, financial services about 15%, business consulting about 10%, and retail about 10%.[24] That usually means employers are buying analysis tied to delivery, operations, risk, and client outcomes rather than building large pure-research teams. The employer base is broad rather than dominated by one company.[12] Over the last 90 days, the most consistently active names in the sample included Lockheed Martin Corporation, Deloitte, Tata Consultancy Services Limited, Dataroles, Kpmg Us, and PepsiCo, Inc.[8] For job seekers, that lowers single-employer risk but raises the importance of tailoring by industry and use case. A smaller but distinctive pocket sits in defense and regulated work. Secret clearance appears in less than 5% of sampled postings, which is not common but can materially separate candidates who already have it.[26]

• Consulting and enterprise services (high): This is the deepest local lane, with IT services and consulting plus business consulting making up a large share of the posting mix, and repeated hiring activity from firms such as Deloitte and Tata Consultancy Services Limited.[24][8]
• Financial services and retail analytics (high): These employers are hiring for decision support, forecasting, customer, pricing, and operational analytics more often than for pure research roles.[24]
• Defense and cleared analytics (moderate): This lane is smaller, but Lockheed Martin Corporation appears among the active employers and secret clearance shows up as a real if uncommon filter.[8][26]
• Pure generative-AI and agentic workflow roles (limited): These roles can pay well, but they are narrower and usually expect evidence of generative AI, machine learning, and tools such as LangChain rather than general interest alone.[9][25]

Where to focus: If you need interviews in the next 90 days, prioritize enterprise analytics roles in consulting, financial services, and retail, and only chase pure AI titles when you can already show production-quality work with Python, SQL, and generative-AI workflows.[24][9]

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 80% of sampled postings, making it the default screening skill across analyst, scientist, and AI-adjacent roles.[9]
• SQL (table stakes): SQL shows up in about 55% of sampled postings and is the easiest way to prove you can work with production business data instead of toy datasets.[9]
• Tableau and data visualization (differentiator): Tableau and data visualization each appear in about 20% of sampled postings, which matters because many local openings are still business-facing rather than research-only.[9]
• Machine learning (differentiator): Machine learning appears in about 30% of sampled postings, so it helps separate candidates from reporting-only applicants without being mandatory for every job.[9]
• Generative AI workflows and LangChain (premium): Generative AI appears in about 20% of sampled postings and LangChain in about 15%, while national skill signals emphasize LLMs, RAG, and agentic workflows for higher-paying data work.[9][25]
• AI literacy, prompt engineering, and workflow automation (differentiator): More than one-third of entry-level jobs now require some AI competency, and employers are explicitly seeking AI literacy, prompt engineering, workflow automation, and ethical judgment.[31]
• Secret clearance (premium): Secret clearance is required in less than 5% of sampled postings, but it can sharply improve odds in defense-linked hiring pockets such as those tied to Lockheed Martin Corporation.[26][8]

Adjacent Roles to Consider

• Business Analyst (bridge): It uses stakeholder management, process mapping, reporting, and requirements work that often sits next to analytics teams.
• FP&A Analyst (pivot): It rewards forecasting, variance analysis, KPI ownership, and executive reporting rather than model-building depth.
• Product Operations Analyst (both): It sits close to experimentation, funnel analysis, dashboarding, and decision support without requiring a full data-science title.
• Supply Chain Analyst (both): It draws on forecasting, optimization, and operations reporting skills that many analytics candidates already have.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two versions: analyst/BI and data scientist/AI, and move Python, SQL, Tableau, machine learning, and generative-AI workflow examples into the top third of the page.[9]
• Build one portfolio piece for a business stakeholder and one for a technical reviewer: a dashboard case and a notebook case.
• Filter searches toward consulting, financial services, retail, and defense-adjacent employers, and drop remote-only assumptions because only about 5% of sampled roles were remote.[24][23]
• Prepare an on-site or hybrid story for Dallas-Fort Worth collaboration expectations because about 90% of sampled roles were not fully remote.[23]

Days 31-60

• Add one proof artifact for AI workflow fluency: an LLM or RAG evaluation, prompt-to-SQL guardrail demo, or LangChain-based internal assistant with validation notes.[9][25]
• Target 20 employers with tailored case studies, especially firms like Lockheed Martin Corporation, Deloitte, Tata Consultancy Services Limited, Dataroles, Kpmg Us, and PepsiCo, Inc. that appeared repeatedly in the local sample.[8]
• If you have domain depth, translate it into analytics language before you apply: forecast accuracy, cycle time, margin lift, fraud reduction, retention, or service levels.

Days 61-90

• If interviews stall, widen the aperture to business analyst, FP&A, product operations, or supply chain roles while keeping a parallel data track.
• Pursue one differentiator that matches your lane: secret clearance if eligible, or a domain-specific case library if you are switching from finance, retail, or operations.[26]
• Review offer strategy against local reality: data scientist pay is strong, but the market is concentrated in experienced roles and active postings stay open around 31 days.[2][27][15]

Methodology and Confidence

This September 2026 report was generated on October 10, 2026. Latest direct national data: September 2026. Latest direct Dallas-Fort Worth-Arlington, TX data: October 2026.

Confidence: Overall confidence: High. Based on 27 direct local occupation data points and 47 total local evidence items with recent coverage.

Limitations

• The newest metro labor-market context here is August 2026, but the most specific local wage benchmarks are annual 2025 occupation estimates and a May 2025 broad occupational wage release, so pay signals lag the live market by several months.[1][2][3]
• Data, Analytics & AI is wider than any single government occupation code; Dallas-Fort Worth has direct BLS data for data scientists, but the broad computer-and-mathematical release does not provide job-title-specific medians or counts for Data Analyst, BI Analyst, or Analytics Engineer.[4][3]
• Statewide labor data was used as a proxy where metro-level Revelio Public Labor Statistics is not published, so Texas direction-of-hiring signals may not match Dallas-Fort Worth exactly.[5][6]
• The Callings.ai job database is a partial, deduplicated sample of online postings, which makes direction of demand, leading employer names, and skill patterns more reliable than exact counts or precise shares.[7][8][9]
• Some niche local pay samples are small: Salient Insights based its Dallas-Fort Worth posted salary medians on seven salary-disclosing Data Analyst postings and eight Data Scientist postings, while its Analytics Engineer count had only one disclosed salary and BI Analyst was not covered.[10]

References

1. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
2. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
3. Bureau of Labor Statistics. Occupational Employment and Wages in Dallas-Fort Worth-Arlington — May 2025 : U.S. Bureau of Labor Statistics · 2026-08 · bls.gov
4. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
5. Reveliolabs. Employment — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
6. Reveliolabs. Job Openings — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
7. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
8. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
9. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
10. Salientinsights. Data & AI Salary Guide 2026: What US Employers Are Paying | Salient Insights · 2026-09 · salientinsights.com
11. Reveliolabs. Mass-layoff Notices — Revelio Public Labor Statistics (RPLS) · 2026-09 · reveliolabs.com
12. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
13. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
14. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
15. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
16. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
17. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-09 · data.bls.gov
18. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov
19. Federal Reserve Economic Data. Consumer Price Index for All Urban Consumers: All Items in U.S. City Average · 2026-08 · fred.stlouisfed.org
20. Federal Reserve Economic Data. Average Hourly Earnings of All Employees, Total Private · 2026-09 · fred.stlouisfed.org
21. Federal Reserve Economic Data. Federal Funds Effective Rate · 2026-09 · fred.stlouisfed.org
22. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
23. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
24. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
25. Learnomate. Top 10 Generative AI Tools for Data Scientists in 2026 | Learnomate · 2026-08 · learnomate.org
26. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
27. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
28. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
29. Bureau of Labor Statistics. Bureau of Labor Statistics Data · data.bls.gov
30. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
31. Campus. AI Skills Employers Want in 2026: Top 5 to Learn · 2026-07 · campus.edu
32. Callings.ai. Callings.ai job-market aggregation · 2026-09 · callings.ai
33. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-08 · data.bls.gov

Live Openings in This Market

This report is published monthly; its companion page tracks the active Data, Analytics & AI openings in Dallas-Fort Worth-Arlington, TX from the live Callings.ai job index. Browse current Data, Analytics & AI openings in Dallas-Fort Worth-Arlington, TX.