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

Executive Verdict

Market rating: competitive | Confidence: High

Seattle is still a real hiring market for Data, Analytics & AI, but it is a selective one. Washington's category-specific active postings were up 22.0% year-over-year in May 2026 while category employment was essentially flat, a pattern that points to continued recruiting without broad-based team expansion.[1][2] Locally, we observed more than 350 postings across more than 150 companies over the last 90 days, but the mix skews toward mid and senior roles and recent Amazon and Meta layoff notices will add experienced competition.[3][4][5][6] Pay remains high, yet most openings are on-site or hybrid rather than remote.[7][8]

Best positioned: Your best odds are strongest if you already have credible Python-and-SQL-heavy analytics or ML experience, can show business impact, and are open to enterprise on-site or hybrid roles.[9][10][8][4]

Main caution: Do not mistake Seattle's headline salary bands for broad access: entry-level roles are only about 5% of the sample, remote roles about 10%, and the official metro wage anchor is for Data Scientists rather than the whole category.[4][8][11]

What Changed Recently

• Washington's Data, Analytics & AI postings are up 22.0% year-over-year, but statewide employment in the category is essentially flat in May 2026.[1][2]: Employers appear willing to open requisitions, but not necessarily to expand teams quickly, so expect more competition per opening and slower offer timing.
• Seattle's metro unemployment rate reached a preliminary 5.1% in April 2026, and Seattle-area CPI rose 4.9% over the prior 12 months.[12][13]: That combination raises the stakes: more job seekers may stay active longer, while compensation has to cover a more expensive local cost base.
• Nationally, April 2026 job openings were 7618 thousand and up 7.3260% year-over-year, but hires were 5116 thousand and down 5.1011% year-over-year.[14][15]: For Seattle applicants, more open roles does not automatically mean faster callbacks or more accepted offers.
• Amazon's Seattle-area WARN notice affecting 2,303 employees reached its final phase on May 26, 2026, and Meta filed a Seattle-area notice affecting 168 employees beginning May 8, 2026.[6][5]: Even if those cuts are not limited to data roles, they increase competition from experienced tech workers in the same metro.
• Seattle startup signals are shifting toward embedded analytics and AI operations: StitcherAI launched with $3 million in pre-seed funding and said its product surfaces cost data inside Snowflake, Tableau, Slack, Jira, Cursor, and OpenAI Codex.[16]: That favors candidates who can build analytics into workflows and operational decisions, not just produce standalone dashboards.

What This Means for You

Entry-Level Candidates

Difficulty: Hard. Entry roles are only about 5% of the local sample, and most openings sit at mid or senior level.[4]

Best target: Target analyst, BI, or operations-facing seats inside enterprise teams, healthcare groups, and internal business units where SQL, Python, and a BI tool matter more than a pure research profile.[10][29][19][9]

Biggest mistake: Chasing only remote AI titles; about 10% of local roles are remote, so that filter removes most of the market.[8]

Next step: Build one portfolio project that combines SQL, Python, and a dashboard, and make it explain a business decision rather than just a model score.

Mid-Career Candidates

Difficulty: Moderate but competitive. The market is strongest for people who already match the local skill mix—Python, SQL, machine learning, and statistical modeling—and can work on-site or hybrid.[9][8]

Best target: Go after enterprise analytics, analytics engineering, and applied data science roles tied to cloud cost, experimentation, or embedded BI use cases.[10][16][18]

Biggest mistake: Presenting yourself as a generic data professional instead of choosing one lane: analyst, analytics engineer, or applied data scientist.

Next step: Rewrite your resume around shipped decisions, revenue or cost impact, and stakeholder ownership.

Career Switchers

Difficulty: Hard unless you can bring domain leverage. Washington's category pay is high, but hiring still skews experienced and recent big-tech layoffs add senior competition.[30][4][5][6]

Best target: Switch through domain-heavy paths such as finance analytics for cloud and AI spend, AI governance, or business operations analytics where prior industry knowledge helps.[16][20][26]

Biggest mistake: Trying to rebrand into ML research with no evidence of production analytics, domain knowledge, or governance awareness.

Next step: Choose one bridge story from your prior field and build a case study around cost, risk, experimentation, or operations.

Salary Reality

high pay highly concentrated

Official local government pay data is strong but narrow: BLS puts the Seattle metro median wage for Data Scientists at $151,320, based on May 2023 data.[11] For fresher directional reads across the broader category, local posted salary ranges center on about $140k to $215k, and Revelio Public Labor Statistics shows Washington's mean offered salary on new openings at ~$149,545 in May 2026 (n=2,820).[7][30]

Seattle pays well for serious data work, and the local posted band is well above Washington's all-occupation offered-salary figure of ~$88,081.[7][30] That said, those pay levels are mostly attached to experienced, technical, or AI-adjacent roles rather than broad-entry analyst jobs.[4][9]

The upside is offset by competition, a heavy mid and senior skew, limited remote availability, and a Seattle CPI reading of 4.9% year-over-year in April 2026.[4][8][13]

Best-paying path: The strongest pay tends to sit in senior data science, AI leadership, and specialized enterprise roles; for example, SAP's Bellevue AI Scientist Senior Manager role asked for 10 years of experience or 8 years with a master's degree and management of more than 100 indirect reports.[38]

Caution: Do not overread top-end figures: the official metro number covers one title, the broader local posted band mixes multiple roles, and national salary guides range widely from about $95,714 to $117,577 for mid-level data analysts to roughly $157,083 to $194,480 for senior data scientists.[11][39]

Where the Opportunities Are Concentrated

Real opportunity is spread across a long tail rather than one dominant employer. We observed more than 350 postings across more than 150 companies over the last 90 days, and the employer mix in the sample is fragmented.[3][31] About 40% of sampled postings come from enterprise employers, and the most active industry buckets are technology at about 40%, information technology at about 25%, software development at about 10%, and healthcare at about 5%.[10][29] The catch is that the market is not evenly accessible. About 45% of postings are mid-level and about 40% senior, while entry roles are only about 5%.[4] The strongest fresh local signals are not about generic reporting work; they point toward analytics built into AI operations and enterprise workflows. Seattle-based StitcherAI launched with $3 million in pre-seed funding and said its product pushes cost data into Snowflake, Tableau, Slack, Jira, Cursor, and OpenAI Codex, while Bellevue-based Contrario explicitly names ML and data-science talent.[16][17] That means the best openings are likely to reward people who can connect data work to cost control, experimentation, governance, and product decisions rather than standalone dashboard ownership.

• Enterprise data science and analytics teams (high): Best bet for volume and pay. About 40% of sampled postings come from enterprise employers, and leading named employers in the sample include Amazon.com, Inc. and Campusbuilding.[10][25]
• Analytics engineering and embedded BI for AI operations (high): Local product signals emphasize cost data embedded inside Snowflake, Tableau, Slack, Jira, Cursor, and OpenAI Codex rather than standalone dashboards, favoring SQL, Python, and dbt-style builders.[16][18]
• Healthcare and regulated decision support (moderate): Healthcare is only about 5% of sampled postings, but responsible AI and governance skills can make you more credible in lower-noise environments.[29][20]

Where to focus: Focus on mid-level analytics engineering or applied analytics roles inside enterprise or AI-infrastructure-adjacent teams, especially if you can show Python, SQL, and BI delivery tied to a cost, growth, or governance outcome.

Skills and Credentials Worth Pursuing

• Python + SQL (table stakes): They are the clearest local baseline: Python appears in about 70% of sampled postings and SQL in about 55%.[9]
• Machine learning + statistical modeling (premium): Machine learning shows up in about 40% of local postings and statistical modeling in about 20%, while fresh Bellevue recruiting signals continue to call out ML and data-science talent.[9][17]
• Analytics engineering (SQL, Python, dbt) (differentiator): Analytics engineering is highlighted as a sought-after 2026 skill, and Seattle startup signals point toward analytics embedded in workflow tools rather than standalone reporting.[18][16]
• Power BI or Tableau (table stakes): Analyst hiring still emphasizes one major BI tool, and local product signals explicitly name Tableau in the working stack.[19][16]
• Cloud platforms + heterogeneous data integration (differentiator): Cloud skills are increasingly essential for data professionals, and local AI-spend products are built across cloud, AI, SaaS, and invoice data sources.[20][16]
• Responsible AI, data ethics, and AI governance (premium): As AI becomes embedded in decisions, responsible AI and unified governance are becoming core expectations rather than niche extras.[20][21]
• LLMs, RAG, NLP, and AI agents (premium): Within AI-focused data science roles, early-2026 demand clusters around LLMs, Generative AI, NLP, RAG, AI agents, and prompt engineering.[22]
• Microsoft PL-300 or DP-600 (differentiator): Local postings rarely require certifications directly—less than 5% mention a certified data analyst credential—but Microsoft's PL-300 and DP-600 can still help you signal practical BI or Fabric capability.[23][24]

Adjacent Roles to Consider

• AI product manager (pivot): Bellevue recruiting signals mention AI product engineers alongside ML and data-science talent, and data teams are increasingly judged by business outcomes rather than technical throughput alone.[17][26]
• FP&A or cloud cost analyst (both): Seattle startup activity is centered on controlling AI and cloud spending through a unified cost model across cloud providers, AI services, SaaS subscriptions, and invoices.[16]
• AI governance or model risk analyst (bridge): Responsible AI and unified AI governance are rising priorities as organizations embed AI in decision-making.[20][21]
• Business operations or strategy analyst (both): Employers increasingly want data work tied to business outcomes, and senior analysts are expected to bring experimentation, modeling, and cloud-adjacent analytics skills.[26][32]

30 / 60 / 90-Day Plan

First 30 Days

• Split your materials into two stories: analyst or analytics-engineering, and applied data science, and front-load Python, SQL, machine learning, and statistical-modeling evidence.[9]
• Add commute and work-mode flexibility to your search strategy; about 65% of local roles are on-site and about 25% hybrid.[8]
• Build one demo that embeds metrics into a working stack such as Snowflake, Tableau, and Slack instead of publishing another standalone dashboard.[16]
• Stop mass-applying to entry AI roles; triage openings by seniority first because about 45% are mid-level, about 40% senior, and only about 5% entry-level.[4]

Days 31-60

• If you lean analyst, finish PL-300 or DP-600 and add a Fabric or Power BI project that mirrors enterprise reporting workflows.[24]
• Create a target list of enterprise employers and AI-infrastructure-adjacent firms; about 40% of local postings come from enterprise employers, and leading named employers in the sample include Amazon.com, Inc. and Campusbuilding.[10][25]
• Publish a short case study on cost, experimentation, or governance outcomes, because 2026 hiring is rewarding business impact more than technical throughput.[26]
• Reach out to recently displaced big-tech professionals for referrals and intel, since Amazon and Meta layoffs may be reshuffling experienced talent locally.[5][6]

Days 61-90

• Add a second specialization on top of general analytics: analytics engineering, AI governance, or LLM-enabled applied data science.[18][20][22]
• Target niche lanes with less generic competition, such as cloud and AI spend analytics, embedded BI, or regulated decision support.[16][20]
• Track your funnel by title family, work mode, and company size, then double down on the combinations that actually return interviews.
• If your search is stalling, broaden into adjacent roles like AI product management, FP&A analytics, or governance-heavy analyst roles rather than waiting only for pure data scientist titles.[17][16][21]

Methodology and Confidence

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

Confidence: Overall confidence: High. Based on 4 direct local occupation data points and 10 total local evidence items with recent coverage.

Limitations

• The clearest official metro wage and employment anchor here is for Data Scientists, and it reflects May 2023 rather than 2026, so newer sub-roles such as analytics engineer or AI engineer are estimated with fresher proxy evidence.[11]
• Washington labor-force and unemployment changes for April 2026 are preliminary, so small month-to-month shifts can revise later.[35][36][37]
• Statewide labor data from Revelio Public Labor Statistics was used as a proxy for Seattle-Tacoma-Bellevue where metro-by-occupation series is not published, so the metro may be stronger or weaker than the state average in a given month.[2][1][30]
• 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 shares.[3][25][9]
• Local WARN notices from Amazon and Meta signal competition for talent, but the notices do not isolate only Data, Analytics & AI roles.[5][6]

References

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11. Bureau of Labor Statistics. Data Scientists · 2024-04 · bls.gov
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38. Jobs. AI Scientist Senior Manager · 2026-05 · jobs.sap.com
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