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

Executive Verdict

Market rating: competitive | Confidence: Medium

Dallas-Fort Worth is still a real market for Data, Analytics & AI, but it is not an easy one to break into. Over the last 90 days, the local sample shows more than 350 postings across more than 200 companies, and hiring is fragmented rather than dominated by one employer.[12][29] Pay remains attractive, with local posted salary ranges centered on about $107k to $176k, but the broader metro labor market has softened: the smoothed unemployment rate was 4.1% in June 2026 and BLS's metro unemployment-rate series was up 17.5000% year over year.[8][31][18]

Best positioned: Candidates who can pair Python and SQL with business-domain credibility and some GenAI workflow skill have the best odds right now.[2]

Main caution: The biggest mistake is treating Dallas as an easy, remote-friendly BI market when only about 15% of sampled roles are entry-level, about 10% are remote, and less than 5% explicitly mention visa sponsorship.[14][13][32]

What Changed Recently

• Texas postings for Data, Analytics & AI are up 28.5% year over year even though statewide employment in the category is down 0.8%.[16][15]: That usually means more visible openings than last year, but not necessarily a looser market; employers may be backfilling, raising skill bars, or being pickier per opening.
• Dallas employers are asking for AI-adjacent tooling, not just classical reporting skills: local postings most often mention Python, SQL, machine learning, Power BI, AWS, prompt engineering, generative AI, and LangChain, and a current Deloitte role in Dallas specifically calls for frontier model platforms such as Anthropic, Google, and OpenAI.[2][5]: If your profile stops at dashboards and ad hoc SQL, you will look dated against candidates who can help deploy or govern AI workflows.
• The local search environment got tougher at the same time. Dallas-Fort Worth's smoothed unemployment rate was 4.1% in June 2026, while the metro unemployment level was 212,830 and up 17.6584% year over year.[31][19]: Even strong candidates should expect slower interview cycles and more competition from adjacent talent pools.
• National hiring is still moving, but cautiously: U.S. job openings totaled 7359 thousand in June 2026, up 2.1516% year over year, while the quits rate was 2% and down 4.7619% year over year.[37][38]: Employers are still opening roles, but fewer workers are voluntarily moving, which is typical of a market where companies retain leverage in hiring.

What This Means for You

Entry-Level Candidates

Difficulty: Hard.

Best target: Aim for business analytics, BI, or analyst roles inside consulting, healthcare, financial services, and energy-adjacent teams where Python, SQL, and Power BI matter more than a pure research data-science background.[1][2][3]

Biggest mistake: Assuming coursework alone will carry you in a market where junior seats are narrow and employers increasingly expect usable AI literacy.

Next step: Build one portfolio case that starts with messy data, uses SQL and Python, ends in a dashboard, and includes a short memo explaining a business decision you would recommend.

Mid-Career Candidates

Difficulty: Moderate to hard.

Best target: Target consulting and enterprise roles where you can show domain fluency plus analytics delivery, especially in technology, IT services, financial services, and healthcare, and be ready to discuss GenAI workflows rather than only reporting stacks.[1][4][5]

Biggest mistake: Positioning yourself as a generic 'data person' instead of a specialist who solves a specific operating problem.

Next step: Rework your resume into two versions: one for analytics leadership and one for AI-enabled delivery, each with outcome metrics, stakeholder scope, and tool depth.

Career Switchers

Difficulty: Hard unless you bring a usable domain.

Best target: The best bridge is operations-facing or business analytics work in your current industry, not a first-jump AI engineer role; the local evidence is stronger for domain-tied analytics than for broad beginner access.[3][1]

Biggest mistake: Leading with certificates and course completions when fewer than 5% of sampled postings explicitly require certifications.[6]

Next step: Turn your prior industry wins into 2-3 analytics case studies with measurable before-and-after results, then apply to roles that value your domain more than your title history.

Salary Reality

high pay highly concentrated

The cleanest local anchor is older government data: data scientists in the metro averaged $116,320 a year in the May 2023 wage survey.[7] Newer but less direct signals point higher: current local posted salary ranges center on about $107k to $176k, Texas Data, Analytics & AI openings show a mean offered salary of about $123,548 in July 2026 (n=5,698), Dallas data-analyst estimates range from about $95,000 to $122,406 depending on source, and a newer proxy based on the May 2025 government release puts Dallas-Fort Worth data scientist pay around $123,150 to $127,750.[8][17][9][10][11]

This is a solid-paying market. Texas new-opening pay for Data, Analytics & AI is well above the state's all-occupation mean offered salary of about $78,561, so the upside is real if you can clear the skill bar.[17]

The offset is access. Only about 15% of sampled roles are entry-level, about 45% are mid-level, about 30% are senior, and only about 10% are remote, so high pay comes with narrower entry points and less flexibility.[14][13]

Best-paying path: The strongest pay tends to sit in specialized data-science and AI-heavy work rather than general reporting, with local category pay bands centered well above typical analyst benchmarks and U.S. Data Scientist midpoint base pay sitting around $153,750 in Robert Half's 2026 guide.[8][39]

Caution: Do not overread the top end. Local posted ranges combine multiple titles and seniority levels, and salary aggregators for data analysts and data scientists are not measuring the exact same role mix.[8][9][10][11]

Where the Opportunities Are Concentrated

Real opportunity is spread across a long tail of employers, not one dominant local buyer. Over the last 90 days, the sample shows more than 350 postings across more than 200 companies, with fragmented hiring and enterprise employers accounting for about 40% of openings.[12][29][30] The most active industries in the sample are technology at about 25%, IT services and consulting at about 20%, then financial services, healthcare, and software development at about 10% each.[1] That mix matters. Dallas appears strongest for candidates who can sell analytics as a business capability inside consulting, financial services, healthcare, and large operating companies, not just as standalone experimentation. Named employers in the recent sample include Deloitte, Matricstek Inc, Infosys, Anblicks, Kpmg Llp, NTT DATA Group, Kpmg Us, and Accenture.[4] Fresh examples reinforce the pattern: Deloitte is recruiting locally for an Agentic AI Engineer tied to healthcare workflows, and Robert Half has a Dallas business analytics contract in energy and natural resources.[5][3] The weak spot is broad-access hiring. Only about 15% of sampled roles are entry-level, while about 45% are mid-level and about 30% are senior, so generalized junior applicants face a narrower lane.[14]

• Consulting and IT services delivery (high): This is the clearest volume segment, with technology at about 25% of sampled postings and IT services and consulting at about 20%; employers such as Deloitte, Infosys, Anblicks, NTT DATA Group, Kpmg Llp, Kpmg Us, and Accenture appear repeatedly in the local sample.[1][4]
• Enterprise analytics in finance, healthcare, and energy-linked business teams (moderate): Financial services and healthcare each represent about 10% of sampled postings, and fresh local examples point to healthcare AI and energy-sector analytics demand rather than only pure tech startups.[1][5][3]
• Entry-level and remote-only job search (limited): This is the tightest segment because only about 15% of sampled roles are entry-level and about 10% are remote.[14][13]

Where to focus: Target mid-career, domain-specific roles in consulting or enterprise teams where Python, SQL, and some GenAI workflow skill are part of the job, not optional.[4][1][2][5]

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 70% of sampled local postings, making it the clearest baseline technical filter in this market.[2]
• SQL (table stakes): SQL shows up in about 50% of local postings and remains the common language across BI, analytics, and data-adjacent roles.[2]
• Power BI (differentiator): Power BI appears in about 20% of sampled local postings, which makes it a useful bridge skill for business analytics and reporting-heavy enterprise teams.[2]
• Machine learning (differentiator): Machine learning shows up in about 20% of local postings and is the clearest bridge from classic analytics into higher-value AI work.[2]
• Generative AI and prompt engineering (premium): Prompt engineering and generative AI each appear in about 15% of sampled local postings, and more than one-third of entry-level jobs nationally now require some level of AI competency.[2][26]
• LangChain, AI agents, and frontier model platforms (premium): LangChain appears in about 15% of local postings, and a current Dallas Deloitte role explicitly emphasizes frontier model platforms such as Anthropic, Google, and OpenAI.[2][5]
• AWS plus responsible AI judgment (differentiator): AWS appears in about 20% of sampled local postings, while national skill signals increasingly emphasize AI ethics and responsible AI as employers move from experimentation to deployment.[2][27][28]

Adjacent Roles to Consider

• AI Product Manager (pivot): Dallas AI hiring is not limited to pure technical seats; one local AI-jobs snapshot lists product management among the most active Dallas AI hiring categories.[36]
• AI Governance or Risk Analyst (pivot): Local AI hiring signals include legal roles, and broader AI trend evidence says trust, governance, and explainability are becoming strategic priorities.[36][28]
• Strategy & Operations Analyst (both): Operations is one of the active Dallas AI-related hiring categories, and a current Dallas contract role ties business analytics directly to an operating industry rather than a pure data team.[36][3]
• Healthcare Workflow Consultant (both): A current Dallas Deloitte opening centers on agentic AI for healthcare workflows, which creates a path for people with healthcare operations experience who are not pure model builders.[5]

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two tracks: business analytics/BI and AI-enabled analytics delivery.
• Build one portfolio piece that proves SQL, Python, and dashboard fluency, and a second that shows an AI-assisted workflow with clear human review and business value.
• Create a target list centered on consulting, IT services, finance, healthcare, and energy-linked employers rather than searching only for generic 'data analyst' titles.
• Change your location and commute settings to include on-site and hybrid roles around Dallas-Fort Worth instead of waiting for remote-only openings.

Days 31-60

• Add a domain version of your story for at least two local demand lanes, such as healthcare and financial services or healthcare and energy.
• Practice interview stories that connect analysis to decisions: cost reduction, cycle time, forecasting accuracy, claims handling, fraud, or customer operations.
• Publish a concise case-study deck with business framing, methodology, metrics, and a 'what I would do next' slide.
• Test contract and consulting routes alongside full-time applications, especially if you are missing direct industry experience.

Days 61-90

• If response rates stay weak, pivot titles toward strategy and operations, AI product, or governance-adjacent roles instead of forcing a pure data-science search.
• Move upmarket on skill positioning by showing one credible cloud, ML, or agentic-workflow example tied to a business use case.
• Use interview feedback to decide whether your blocker is technical depth, business framing, or domain credibility, then fix only that gap instead of collecting more random courses.
• Reassess salary targets by title family so you can stay competitive for bridge roles while preserving upside for specialized paths.

Methodology and Confidence

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

Confidence: Overall confidence: Medium. Direct local labor data exists, but several role and pay conclusions rely on proxy signals and category-level inference.

Limitations

• The best hard local wage anchor in this bundle is a metro data-scientist estimate from May 2023, so newer Dallas pay statements rely more on posted-salary samples and salary aggregators than on a current government metro wage release.[7][8][9][10][11]
• This category bundles data analyst, BI, data scientist, analytics engineer, ML, AI, and operations research work, so any one title is only a proxy for the broader market and sub-role conditions can differ meaningfully.
• The Callings.ai job database is a partial, deduplicated sample of online postings, so it is more reliable for spotting direction, leading employer names, skill patterns, seniority mix, and work-arrangement mix than for treating the exact counts or shares as a full census of Dallas hiring.[12][4][13][14][2]
• Statewide occupation data from Revelio Public Labor Statistics was used as a proxy where metro-level occupation trends were not published, so Texas direction signals may not match Dallas exactly.[15][16][17]
• Several June 2026 BLS year-over-year changes for Dallas and Texas labor-market context are still preliminary, so the local softening described here could be revised in later releases.[18][19][20][21][22][23]

References

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2. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
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4. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
5. Deloitte. Check out this job at Deloitte, Agentic AI Engineer — Healthcare AI · 2026-08 · apply.deloitte.com
6. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
7. Bureau of Labor Statistics. Data Scientists · 2024-04 · bls.gov
8. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
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10. Randstadusa. data analyst salaries in dallas, texas | Randstad USA · 2026-01 · randstadusa.com
11. Elevatedtech. Data Scientist Salary in Dallas-Fort Worth-Arlington (2026): $127,750 Median, $123,150 Mean · 2026-01 · elevatedtech.us
12. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
13. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
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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 · 2026-06 · data.bls.gov
20. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
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25. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-07 · data.bls.gov
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31. Federal Reserve Economic Data. Unemployment Rate in Dallas-Fort Worth-Arlington, TX (MSA) · 2026-07 · fred.stlouisfed.org
32. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
33. Dallasnews. Client Challenge · 2026-07 · dallasnews.com
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35. Skillsyncer. 2026 Tech Layoffs Tracker: 205,832 Workers Impacted · 2026-08 · skillsyncer.com
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38. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-06 · data.bls.gov
39. Robert Half. 2026 Tech and IT Salaries and Compensation Trends · 2025-10 · roberthalf.com

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.