Is Data, Analytics & AI a Good Job Market in Seattle-Tacoma-Bellevue, WA?

Executive Verdict

Market rating: competitive | Confidence: High

Seattle is still a good place to pursue Data, Analytics & AI work if you already have in-demand skills, but it is not an easy market. Washington's Data, Analytics & AI employment was essentially flat year over year in July 2026 while active postings were up 14.5%, which points to continued hiring activity without broad-based headcount expansion.[17][18] Locally, we observed more than 300 postings across more than 150 companies over the last 90 days, but the mix skews toward mid and senior roles, with only about 10% entry-level, about 55% on-site, and about 10% remote.[14][1][16] Pay is strong, especially for data science and AI-heavy paths, but Seattle's cost of living index of 145.7 means high salaries do not automatically translate into easy quality-of-life gains.[9][30]

Best positioned: The best odds right now go to candidates with Python, SQL, and machine-learning depth who can also handle business-facing analytics work, partner with product and engineering teams, and accept on-site or hybrid roles.[5][6][16]

Main caution: The biggest mistake is assuming this is a broad remote-friendly market for beginners; only about 10% of sampled roles were entry-level and only about 10% were remote.[1][16]

What Changed Recently

• Washington's Data, Analytics & AI postings were up 14.5% year over year in July 2026 even though occupation employment was essentially flat statewide.[18][17]: That usually means more live openings are circulating than a simple headcount number suggests, but it also means many employers are replacing, upgrading, or being selective rather than expanding indiscriminately.
• Seattle-Tacoma-Bellevue unemployment reached 5.0% in June 2026, while Washington's unemployment rate was 5.2% and up 15.5556% year over year.[10][20]: A softer local labor backdrop raises applicant competition, so strong portfolios and clear domain fit matter more than they did in a tighter market.
• Current local opportunity is tilting toward experienced candidates: about 45% of sampled postings were mid-level, about 40% senior, and about 10% lead+, versus about 10% entry-level.[1]: If you are early-career, you should target analyst roles with clear business ownership rather than wait for idealized junior AI jobs.
• Nationally, JOLTS showed 7.359 million openings and a 4.4% openings rate in June 2026, but the hires rate held at 3.4% year over year and the quits rate was 2%, down 4.7619% year over year.[38][24][25][26]: For Seattle candidates, that points to employers still posting roles but moving carefully, so interview cycles may stay slow and choosy even when openings look plentiful.

What This Means for You

Entry-Level Candidates

Difficulty: Hard. The local mix is thin for beginners, with only about 10% of sampled roles at entry level, and more than one-third of entry-level jobs nationally now require some AI competency.[1][2]

Best target: Business-facing analyst openings where you can show dashboarding, KPI reporting, A/B testing, forecasting, and stakeholder communication rather than pure reporting work.[3][4]

Biggest mistake: Applying as a generic 'junior data analyst' without a portfolio that shows decisions, tradeoffs, and business recommendations.

Next step: Build two portfolio pieces in the next month: one KPI/forecasting case and one experiment-analysis case, each with a short memo aimed at a nontechnical manager.

Mid-Career Candidates

Difficulty: Moderate to competitive. This is the part of the market with the most visible local demand.[1]

Best target: Analytics engineer, lead analyst, and senior data roles that combine Python/SQL depth with reusable frameworks, DBT-style workflows, and product or engineering partnership.[5][6]

Biggest mistake: Presenting yourself as a tool operator instead of someone who improves decision quality, data models, and cross-functional execution.

Next step: Rework your resume around shipped outcomes: experiments influenced, forecast accuracy improved, pipelines simplified, and stakeholder decisions changed.

Career Switchers

Difficulty: Moderate to hard. Switching is more realistic when you bring domain credibility from healthcare, finance, retail, or adjacent operations work.[7][6]

Best target: Domain-linked analytics roles where prior industry knowledge can offset weaker pure-data tenure, especially in healthtech or enterprise business analytics.[6][8]

Biggest mistake: Trying to jump straight into AI/ML branding without first proving you can frame problems, define metrics, and communicate recommendations.

Next step: Package your prior domain into a data story: pick one business problem from your old field, model the KPIs, and show how you would turn analysis into an operating decision.

Salary Reality

high pay highly concentrated

The strongest observed local benchmark is the BLS mean annual wage for data scientists in Seattle-Tacoma-Bellevue at $160,460.[9] That does not describe every role in this category, so proxy signals help fill in the rest of the market: sampled posted salary ranges in Seattle-Tacoma-Bellevue center on about $137k to $215k, Bellevue data analyst estimates run about $88,582–$131,793, and some Seattle data analyst listings span roughly $70K–$95K while others reach $176K–$220K.[11][39][3]

This is a high-pay market by U.S. standards, and Washington's mean offered salary on new Data, Analytics & AI openings was about $136,999 in July 2026 versus about $92,906 across all occupations statewide.[19] In practice, Seattle pays for specialization, business impact, and seniority more than for title alone.

The upside is offset by a high local cost of living, a senior-heavy hiring mix, and a limited remote share.[30][1][16] You can earn well here, but the market expects stronger evidence of scope and autonomy than many lower-cost metros do.

Best-paying path: The strongest pay tends to sit in AI/ML-heavy or top-tier enterprise roles: Robert Half projects Seattle AI/ML Engineer pay around $172,860 at the 25th percentile, $220,268 at midpoint, and $249,293 at the 75th percentile, while Levels.fyi reports Microsoft data analyst median total compensation at $199K in Greater Seattle.[40][12]

Caution: Do not overread the top end. Some figures are posted ranges, some are estimated salary aggregations, and some mix base salary with total compensation, so a headline number is not the same as what a typical applicant should expect.[11][12][13]

Where the Opportunities Are Concentrated

Real opportunity is broad across employers, but not broad across candidate profiles. Over the last 90 days, we observed more than 300 local postings across more than 150 companies, and the employer mix was fragmented rather than dominated by one buyer.[14][29] Even so, about 55% of sampled postings came from enterprise employers, so the center of gravity is still larger companies with more formal hiring bars.[8] Industry concentration is clearest in technology-facing business work. The most-active posting industries in the sample were technology at about 35%, software development at about 20%, internet publishing/broadcasting/web search portals at about 10%, financial services at about 10%, and retail at about 10%.[7] Current listings also show activity in Bellevue and Seattle, Amazon is actively hiring Data Scientist and Data Engineer roles in Seattle, and Arcadia is hiring a Lead Analyst, Analytics role in healthtech that emphasizes DBT, reusable frameworks, and product-engineering partnership.[35][36][6] The most important concentration pattern is seniority, not sector. About 45% of sampled roles were mid-level, about 40% senior, and about 10% lead+, while only about 10% were entry-level.[1] That means the market is healthiest for people who can already own ambiguous business questions or production-grade analytics workflows.

• Enterprise tech and internet platforms (high): This is the largest visible pool, with technology and software-related employers accounting for much of the sample and enterprise firms making up about 55% of postings.[7][8]
• Business-facing analytics and analytics engineering (high): Local postings emphasize dashboarding, KPI reporting, A/B testing, forecasting, data pipeline improvement, and business recommendation skills rather than narrow report building alone.[3][4]
• Healthtech analytics (moderate): Arcadia's Seattle healthtech hiring highlights active demand for lead analytics work tied to DBT, reusable frameworks, and cross-functional partnership.[6]
• Pure entry-level remote analyst work (limited): This exists, but it is a small slice of the market because entry-level roles are only about 10% of the sample and remote roles are also about 10%.[1][16]

Where to focus: Focus your search on enterprise, business-facing analytics and analytics-engineering roles in tech-adjacent sectors, and be willing to work on-site or hybrid.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appeared in about 75% of sampled local postings, making it the clearest baseline skill across the category.[5]
• SQL plus business storytelling (table stakes): SQL showed up in about 55% of local postings, and employers are still competing for analysts who pair SQL with storytelling and stakeholder communication.[5][4]
• Machine learning and statistical modeling (differentiator): Machine learning appeared in about 35% of local postings, while statistical modeling appeared in about 15%, which separates broader data roles from purely descriptive analysis work.[5]
• DBT and reusable analytics frameworks (differentiator): Local lead-level analytics hiring explicitly calls for DBT, reusable frameworks, and product-engineering partnership, signaling demand for modern analytics engineering habits.[6]
• AI literacy and prompt engineering (differentiator): AI skills appear in nearly 45% of data and analytics postings nationally in 2026, and prompt engineering remains one of the fastest-growing AI skills.[2]
• MLOps workflow: experiment tracking, model registries, and drift detection (premium): These are described as table stakes for MLOps in 2026, which matters for higher-end AI and production-model roles.[27]
• Governance-first AI work: auditability, explainability, and bias testing (premium): AI governance requirements are pushing employers toward auditability, explainability, and bias testing, creating value for candidates who can handle regulated or risk-sensitive deployments.[27]
• AWS certification (differentiator): AWS-certified credentials were mentioned in less than 5% of sampled local postings, so they are not a universal gate, but they can still help as a signal when cloud exposure is otherwise hard to prove.[28]

Adjacent Roles to Consider

• Data Product Manager (both): Data product manager is one of the newer roles emerging around the data science career path, and local lead analytics hiring already emphasizes product-and-engineering partnership.[37][6]
• AI Model Auditor / Responsible AI Analyst (pivot): Governance-first AI work is becoming necessary, with auditability, explainability, and bias testing now important in 2026.[27]
• Strategy & Operations Analyst (bridge): Demand remains strongest for analysts who translate data into business recommendations rather than only build reports, which aligns naturally with strategy and operations work.[4]
• Product Analyst in a non-data team (bridge): Local openings stress A/B testing, KPI reporting, forecasting, and stakeholder communication, which maps well to product-adjacent analyst seats.[3][4]

30 / 60 / 90-Day Plan

First 30 Days

• Cut your resume into two versions: one for business-facing analytics and one for data science/AI-heavy roles.
• Build or refresh two portfolio artifacts that show decisions, not just charts: one KPI/forecast case and one experiment or causal-analysis case.
• Rewrite LinkedIn and resume bullets around outcomes, including decisions influenced, forecast improvement, experiment impact, or time saved.
• Filter your target list toward enterprise, healthtech, finance, and retail employers where local demand is visible, and stop spending most of your time on remote-only searches.

Days 31-60

• Add one workflow upgrade that changes your level signal: DBT project structure, reusable analytics framework, or model monitoring demo.
• Create a short presentation deck for one project aimed at a VP, PM, or operations leader to prove stakeholder communication.
• Run a targeted application sprint into Bellevue and Seattle hybrid roles instead of broad national one-click applications.
• If you are mid-career, collect manager or partner testimonials that prove cross-functional ownership and mentoring.

Days 61-90

• Choose a lane and commit: business analytics, analytics engineering, data science, or AI/ML-heavy work.
• If your interviews are weak, practice case-style communication with live business recommendations rather than more coding drills alone.
• If you are blocked at the top of the funnel, add a visible credibility signal such as an AWS credential, a governance/Responsible-AI project, or a production-style MLOps demo.
• If you are entry-level or switching careers, widen target titles into product-adjacent and operations-adjacent analyst roles instead of waiting for ideal junior AI postings.

Methodology and Confidence

This July 2026 report was generated on August 10, 2026. Latest direct national data: August 2026. Latest direct Seattle-Tacoma-Bellevue, WA data: August 2026.

Confidence: Overall confidence: High. Based on 5 direct local occupation data points and 12 total local evidence items with recent coverage.

Limitations

• The strongest metro pay benchmark in this report is the BLS Seattle occupational wage release, but that wage snapshot is from May 2025, so it is older than the June 2026 unemployment and payroll context used elsewhere here.[9][10]
• This category bundles several related job families, including analysts, data scientists, statisticians, operations research roles, and AI/ML-heavy jobs, so no single title or salary figure should be read as the whole Seattle market.[9][11]
• Some compensation examples come from posted salaries or salary aggregators, which can mix base salary with total compensation and can differ materially from government wage measures.[11][12][13]
• The Callings.ai job database is a partial, deduplicated sample of online postings, so it is more reliable for spotting demand direction, leading employer names, seniority mix, work-arrangement patterns, and skill themes than for exact market counts or exact employer share in Seattle.[14][15][16][1][5]
• Washington occupation-level hiring and salary signals were used as a proxy where metro-level occupation series were not available, and some state and national labor figures cited here are preliminary and may later be revised.[17][18][19][20][21][22][23]

References

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5. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
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8. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
9. Bureau of Labor Statistics. Occupational Employment and Wages in Seattle-Tacoma-Bellevue — May 2025 · 2026-03 · bls.gov
10. Bureau of Labor Statistics. U.S. Bureau of Labor Statistics · 2026-07 · bls.gov
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34. Callings.ai. Callings.ai job-market aggregation · 2026-07 · callings.ai
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39. Comptool. Compensation Management Tools | CompTool · 2026-01 · comptool.com
40. Robert Half. Staffing, Recruitment & Job Search · 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 Seattle-Tacoma-Bellevue, WA from the live Callings.ai job index. Browse current Data, Analytics & AI openings in Seattle-Tacoma-Bellevue, WA.