Is Data, Analytics & AI a Good Job Market in Austin-Round Rock-San Marcos, TX?

Executive Verdict

Market rating: competitive | Confidence: High

Austin is still a live market for Data, Analytics & AI, with more than 75 postings across more than 75 companies over the last 90 days and current openings at DFPS, UT Austin, and ERS.[11][12][13][14] Pay is attractive for qualified candidates: local data scientist wages run from $86,382 at the 25th percentile to $157,290 at the 75th percentile, while posted salaries across the broader category center on about $120k to $160k.[15][16] But the market is not easy: about 50% of sampled postings are senior, only about 10% are entry-level, about 15% are remote, and local information employment was down -4.3% year over year even as total metro payrolls rose 1.4%.[17][18][9][19] This is a competitive market, not a shrinking one.

Best positioned: Mid-to-senior candidates who can show Python and SQL plus machine learning, dashboarding, and pipeline work have the best odds, especially if they are open to on-site or hybrid Austin roles.[20][12][13][17][18]

Main caution: Do not assume Austin's tech brand means easy entry: entry roles are a small slice of the market, remote roles are limited, and typical active postings have been open around 56 days.[17][18][21]

What Changed Recently

• Late-March and early-April openings at DFPS, UT Austin, and ERS show live hiring in Austin across government and higher education, not just private tech.[12][13][14]: That widens your target list and rewards candidates who can work across analytics, dashboards, ETL, and applied ML rather than chasing only brand-name tech firms.
• Austin's broader economy is still adding jobs, but local information employment fell -4.3% year over year while total metro payrolls rose 1.4%.[9][19]: That mix suggests selective hiring: product-tech teams may stay cautious even while adjacent employers in finance, professional services, education, and health keep hiring.
• The local posting mix is staying senior-heavy and mostly in-person, with about 50% senior, about 10% entry, about 60% on-site, about 25% hybrid, and about 15% remote.[17][18]: If you are junior or remote-only, you need tighter targeting and faster proof of capability than you would in a looser market.
• National hiring is cooler even though it has not broken down: U.S. unemployment was 4.3% in March 2026, total nonfarm payrolls were up only +0.2% year over year, and the national hires rate was 3.1% in February, down -8.8% year over year.[1][2][3]: In Austin, that usually shows up as slower interview cycles, more competition per opening, and less patience for generalist resumes.
• Oracle reported Austin-based layoffs in March as part of a broader AI and cost-cutting shift.[8]: Big employers may still cut legacy teams while selectively adding people who fit AI, data-platform, and infrastructure priorities.

What This Means for You

Entry-Level Candidates

Difficulty: High.

Best target: Aim at BI analyst, junior data analyst, reporting analyst, program analyst, and university or state-agency roles where dashboards, SQL, and business communication matter more than deep model research.

Biggest mistake: Applying as a generic 'data person' without a portfolio that proves you can answer a business question, clean data, and ship a dashboard.

Next step: Build three artifacts fast: one SQL case study, one Tableau or Power BI dashboard, and one Python notebook that turns raw data into a recommendation.

Mid-Career Candidates

Difficulty: Moderate.

Best target: Target senior analyst, data science analyst, analytics engineer, and ML-enabled analyst roles tied to revenue, operations, healthcare, or public programs.

Biggest mistake: Leading with tools only instead of showing measurable outcomes like forecast accuracy, retention lift, fraud reduction, or process savings.

Next step: Rewrite your resume around 4-6 business outcomes, then create separate application versions for analyst, analytics-engineer, and applied-data-science roles.

Career Switchers

Difficulty: Moderate to high.

Best target: Use domain adjacency: finance into revenue or risk analytics, healthcare into clinical or ops analytics, supply chain into logistics analytics, and public policy into program data roles.

Biggest mistake: Trying to compete head-on for AI engineer titles before you have shipped real projects with data pipelines or model deployment.

Next step: Pick one domain you already know, then build a portfolio project in that domain so your subject knowledge becomes an advantage instead of a footnote.

Salary Reality

high pay highly concentrated

The clearest local observed pay data is for data scientists, where the Austin wage distribution runs from $86,382 at the 25th percentile to $122,429 at the median and $157,290 at the 75th percentile as of February 2026.[15] That is occupation-specific government data. Separate from that, posted salaries across the broader local Data, Analytics & AI category center on about $120k to $160k, with a broader 25th-75th band of about $90k to $190k; treat that as directional because it reflects a partial postings sample rather than all hires.[16]

Austin can pay very well, but the strongest money is concentrated in more technical sub-roles than basic reporting. Recent local openings emphasize machine learning models, dashboards, Databricks, ETL, and data pipelines rather than spreadsheet-only analysis.[12][13][20]

The upside comes with tradeoffs: about 50% of local postings skew senior, only about 15% are remote, and typical active postings sit open around 56 days, which signals a market where employers can wait for close matches.[17][18][21]

Best-paying path: The best-paying path is usually the intersection of data science and applied AI: local data scientist pay reaches $157,290 at the 75th percentile, while national 2026 guides place mid-to-senior data scientists around $138,054-$194,480 and AI engineers around $167,274 on average.[15][22][23]

Caution: Do not read top-end figures as normal outcomes. The local government example in this bundle is a Data Analyst V role paying $6,377.50 - $8,581.66 monthly, which is well below elite AI-engineer numbers and a reminder that title, sector, and seniority drive pay more than the category label alone.[12]

Where the Opportunities Are Concentrated

Real opportunity is spread across a long tail rather than one giant employer. In the local postings sample, hiring is fragmented, with more than 75 postings across more than 75 companies; the most consistently active names include Apple, RevOps Advisor, and News Corp at around 5 postings each.[11][24][10] The industry mix leans heavily toward information technology and technology, which together account for about 75% of sampled postings, with smaller pockets in engineering, financial services, and media and information services.[32] At the same time, some of the most concrete live openings in Austin are outside classic big-tech product teams. DFPS is hiring for Databricks, ETL, and Tableau or Power BI work, UT Austin is recruiting for advanced analytics, machine learning models, and data pipelines, ERS has a live data analyst role, and eBay is hiring a Senior Data Science Analyst on its Shipping team.[12][13][14][28] That makes public sector, university, healthcare-adjacent, and operations analytics worth real attention, not just tech brands. Sector employment trends point to where demand may hold up better. Local financial activities employment rose 3.9% year over year, professional and business services rose 1.6%, and education and health services rose 2.6%, while information fell -4.3%.[29][33][34][9] For job seekers, that means customer, operations, risk, and domain analytics may be easier entry points than pure platform-company hiring.

• Tech and product analytics (high): This is still the biggest lane in the local sample: information technology is about 50% of postings and technology is about 25%, with active names including Apple, News Corp, and AMD.[32][24]
• Public sector and higher-ed analytics (moderate): Current Austin openings at DFPS, UT Austin, and ERS point to steady demand for analysts who can build dashboards, pipelines, and operational reporting in mission-driven settings.[12][13][14]
• Finance, revenue, and operations analytics (moderate): Financial services are only about 5% of sampled postings, but local financial activities employment was up 3.9% year over year, which makes this a practical lane for candidates with forecasting, KPI, pricing, or risk skills.[32][29]
• Entry-level reporting-only analyst work (limited): This is the weakest lane right now because only about 10% of sampled postings are entry-level, and AI has automated roughly 30-40% of the mechanical analyst tasks that used to fill many junior roles.[17][25]

Where to focus: Prioritize roles where you can show Python plus SQL plus one delivery layer such as dashboards, ETL, or ML pipelines, and widen your target list beyond consumer tech to state agencies, universities, healthcare, and finance.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appears in about 65% of local postings, making it the closest thing to a baseline screening skill in this market.[20]
• SQL (table stakes): SQL shows up in about 40% of local postings, and employers still need people who can query, validate, and explain data correctly even as AI automates some routine tasks.[20][25]
• Tableau or Power BI (differentiator): DFPS explicitly asks for Tableau or Power BI dashboard experience, which is a strong sign that stakeholder-facing reporting still matters in Austin.[12]
• Machine learning models and data pipelines (premium): UT Austin's current Data Science Analyst II opening centers on advanced analytics, machine learning models, and data pipelines, so shipping end-to-end work matters more than isolated notebook experiments.[13]
• Databricks, Azure Data Factory, and ETL tooling (premium): A current Austin state-agency role requires Azure Databricks plus ETL tools such as Informatica and Azure Data Factory, which points to demand for platform-aware analysts, not just model builders.[12]
• PyTorch, LangChain, vector databases, and TensorFlow (differentiator): Local postings request PyTorch at about 25%, LangChain at about 20%, vector databases at about 20%, and TensorFlow at about 15%, showing that AI-adjacent tooling is moving into mainstream data hiring.[20]
• Machine learning certification (differentiator): Only about 5% of local postings explicitly require a machine learning certification, so a cert alone will not carry your application, but certifications are increasingly treated as hiring filters and skill-validation signals in 2026.[26][27]

Adjacent Roles to Consider

• BI Analyst / Reporting Analyst (bridge): Current Austin demand still includes dashboarding and analytical storytelling, especially in roles tied to Tableau, Power BI, and stakeholder reporting.[12][28]
• Analytics Engineer / Data Engineer (both): Recent Austin openings emphasize ETL, Databricks, Azure Data Factory, and data pipelines, which makes pipeline ownership a natural adjacent path.[12][13]
• Financial Analyst / Revenue Analyst (pivot): Local financial activities employment was up 3.9% year over year, which supports demand for KPI, forecast, pricing, and business-planning work.[29]
• Business Systems Analyst / Computer Systems Analyst (bridge): This is a practical route for candidates whose strength is translating business needs into data and systems requirements rather than building models.

30 / 60 / 90-Day Plan

First 30 Days

• Split your resume into two versions: one for analyst and BI roles, one for data science and analytics-engineer roles.
• Build one Austin-ready portfolio project that includes Python, SQL, and a dashboard, then publish both the code and a one-page business summary.
• Create a target list of 25 employers across state agencies, universities, healthcare, finance, and tech instead of waiting on a few big brands.
• Search for roles using stack keywords, not just titles: Databricks, Power BI, Tableau, ETL, data pipeline, ML model, and analytics engineer.
• Decide now whether you can do on-site or hybrid work and state it clearly in your applications.

Days 31-60

• Add one end-to-end pipeline project using a cloud or warehouse tool so you can show more than notebook analysis.
• Reach out to hiring managers or team leads with a short note that links directly to one relevant project, not a generic introduction.
• Practice case stories around business impact: how you improved a metric, reduced manual work, or influenced a decision.
• If you are a career switcher, build your second portfolio piece in the domain you already know so your background becomes a signal, not a hurdle.

Days 61-90

• If interviews are not converting, narrow your lane to one of three tracks: BI/reporting, applied data science, or analytics engineering.
• Add either a machine learning certification or a stronger cloud-data project only if it closes a real gap in the jobs you are targeting.
• Expand to adjacent roles like revenue analytics, systems analysis, or operations analytics rather than waiting for a perfect title match.
• Review every rejection for pattern: missing domain depth, missing pipeline work, or weak communication, then rebuild your portfolio around that gap.

Methodology and Confidence

This March 2026 report was generated on April 22, 2026. Latest direct national data: April 2026. Latest direct Austin-Round Rock-San Marcos, TX data: April 2026.

Confidence: Overall confidence: High. Based on 21 direct local occupation data points and 48 total local evidence items with recent coverage.

Limitations

• The most precise local wage series in this report is for data scientists, which is only one slice of Data, Analytics & AI; analyst, BI, analytics-engineer, and AI-engineer pay can differ a lot.
• Some local labor-market context series here are from January 2026, and the unemployment year-over-year changes were still preliminary when this report was produced, so later revisions may slightly change the short-term picture.
• The Callings.ai job database is a partial, deduplicated sample of online postings, so direction of demand, leading employer names, seniority mix, and skill patterns are more reliable than exact counts or exact market share.
• Several March WARN notices in the Austin metro were outside this occupation group, so they should be read as background market pressure rather than direct proof that local data teams are broadly shrinking.
• Recent live postings are useful for reading employer skill demand, but any one posting can reflect a single team's stack and may close faster than broader labor data updates.

References

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