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 market for Data, Analytics & AI, but it is a competitive one in April 2026. Washington-level Data, Analytics & AI postings were up 13.6% year over year in April 2026 while employment in the occupation family was essentially flat, which suggests ongoing openings without broad headcount expansion.[3][4] Locally, the broader metro is softer: Seattle unemployment was 4.9% in March 2026, metro employment was down 1.4% year over year, and only about 10% of sampled postings were entry-level.[8][9][10] Pay is still attractive—local posted salary ranges center on about $143k to $215k—but the best odds sit with experienced candidates who can do more than dashboard maintenance.[11]

Best positioned: Candidates with a few years of experience in Python, SQL, machine learning, and production-minded analytics or AI work have the best odds, especially if they can target enterprise employers and accept on-site or hybrid roles.[12][13][14][10]

Main caution: The biggest trap is assuming Seattle's tech brand means abundant remote junior openings; in the local sample about 60% of roles were on-site, about 30% hybrid, about 10% remote, and about 10% were entry-level.[14][10]

What Changed Recently

• Revelio Public Labor Statistics shows Washington Data, Analytics & AI postings up 13.6% year over year in April 2026 while employment in the occupation family was essentially flat.[3][4]: More jobs are being advertised, but that does not look like broad team expansion. Expect openings, but also heavier competition for each seat.
• Seattle's broader labor market weakened year over year: metro unemployment reached 4.9% in March 2026, the unemployment level rose 11.0% year over year, and employment level fell 1.4%.[8][21][9]: A softer metro backdrop usually means more applicants per role, longer hiring cycles, and less room for weak positioning.
• The local opportunity set is wide but skewed senior and in-person: more than 350 postings appeared across more than 175 companies over the last 90 days, yet about 50% were senior, about 10% were entry-level, and only about 10% were remote.[22][10][14]: Seattle still has volume, but the practical market is much smaller if you need remote work or are trying to break in.
• Layoff activity keeps feeding the local talent pool. Seattle-area WARN notices included Amazon.com Services LLC with 2,303 affected employees, T-Mobile USA with 1,476, Oracle America with 491, Meta Platforms with 168, Expedia with 162, and Wescom Financial with 72 across notices tied to 2026 effective dates.[23][24][25][26][27][28]: Even if your target role is not directly hit, those notices increase competition from experienced tech workers across analytics, data, and AI-adjacent functions.
• National hiring is still moving, but with more caution than a year ago: U.S. job openings were down 3.3% year over year in March 2026, hires were up 3.0%, layoffs and discharges were 1.2%, and CPI was up 3.1%.[29][30][31][5]: Companies are still hiring, but they are screening more tightly and watching budgets, which raises the bar for Seattle candidates who want premium pay.

What This Means for You

Entry-Level Candidates

Difficulty: Hard. Only about 10% of sampled local roles were entry-level, and AI is automating 30-40% of traditional analyst tasks such as SQL writing, cleaning, and standard reporting.[10][15]

Best target: Target analyst-to-scientist bridge roles, BI-heavy roles, or analytics support roles where you can prove Python, SQL, data visualization, and statistical modeling instead of Excel-only reporting.[12]

Biggest mistake: Applying to generic data analyst postings without a portfolio that shows business framing, stakeholder communication, and at least one automation or modeling example.[16][15]

Next step: Build two tight case studies in the next 30 days: one BI/dashboard project using SQL plus Power BI, and one automation or forecasting project in Python, then apply only where your project closely matches the job.

Mid-Career Candidates

Difficulty: Moderate but selective. The market skews toward experienced hiring, with about 40% of sampled roles at mid level and about 50% at senior level.[10]

Best target: Go after enterprise and applied-AI teams that want Python, SQL, machine learning, and model-to-production capability, especially in information technology, technology, and large-company settings.[12][17][13]

Biggest mistake: Presenting as a generalist reporter when employers are rewarding candidates who can automate workflows, query cloud data platforms, and own decisions end to end.[16][18]

Next step: Split your resume into two versions—analytics leadership and AI/ML delivery—and add one recent project that shows business impact, deployment choices, and cross-functional communication.

Career Switchers

Difficulty: Moderate to hard. Most local openings are not training roles, and among postings that state an education requirement the common baseline is a bachelor's degree or higher.[19][10]

Best target: Switch into domain-fluent analytics in an industry you already know—finance, operations, healthcare, or customer analytics—rather than trying to leap straight into pure ML titles.

Biggest mistake: Trying to compete on tools alone without translating your prior experience into measurable business questions, metrics, and decision support.

Next step: Create a transition story around one domain problem you already understand, then earn a focused credential such as PL-300 or the IBM Data Analyst certificate only if it helps you demonstrate that story.[20]

Salary Reality

high pay highly concentrated

Observed local posting data shows Seattle-Tacoma-Bellevue salary ranges centered on about $143k to $215k, with hourly roles centered on about $35 to $45 / hour.[11][40] Directional cross-checks are lower: Built In put the average Data Scientist salary in Seattle at $133,749 in May 2026, while Revelio Public Labor Statistics estimated mean offered pay on new Washington Data, Analytics & AI openings at ~$149,750 (n=3,082) and national new-opening pay at ~$124,141 (n=153,010).[41][42]

This is still a high-pay market, but it pays for scope and scarcity more than for title alone. Seattle compensation is strongest when the job blends analysis, modeling, data engineering, and stakeholder ownership rather than pure reporting.

The upside is offset by a tougher funnel: the metro employment base is softer, senior roles dominate the sample, and only a small share of roles are remote.[9][10][14]

Best-paying path: The strongest pay tends to sit in senior AI/ML, analytics engineering, and enterprise data roles, especially inside large-company environments where about 40% of sampled postings came from enterprise employers.[13][11]

Caution: Do not read the top end of posted bands as typical take-home pay. The broader 25th-75th local band stretches from about $120k to $258k, which means employer type, seniority, and specialty drive very different outcomes.[11]

Where the Opportunities Are Concentrated

Real opportunity is spread across a long tail rather than one dominant employer. Over the last 90 days, more than 350 Data, Analytics & AI postings appeared across more than 175 companies in Seattle-Tacoma-Bellevue, and the employer mix in the sample was fragmented.[22][33] The most consistently active named employers were Campusbuilding, Amazon Fulfillment Technologies Robotics, and Amazon Science, but together they still do not define the whole market.[36] The work is concentrated more by employer type and industry than by a single company. About 40% of sampled postings came from enterprise employers, and the most-active industries were information technology at about 40%, technology at about 30%, and healthcare at about 5%.[13][17] That matches the broader local economy: Seattle's Professional and Business Services employment was up 0.6% year over year in March 2026 while Information employment was down 0.2%, so the safer bet is not "big tech only" but data roles embedded in large service, platform, and applied-product organizations.[7][37] Because only about 10% of roles were entry-level and about 60% were on-site, the practical opportunity pool is narrower than the raw volume suggests.[10][14]

• Enterprise platform and corporate data teams (high): Large employers account for about 40% of sampled postings, making enterprise data platforms, internal analytics, and decision-support teams the broadest target set.[13]
• Applied AI, robotics, and science-led teams (moderate): Named activity from Amazon Fulfillment Technologies Robotics and Amazon Science points to continued demand for model-driven, experimentation, and research-adjacent work, though these roles are more selective.[36]
• Healthcare and regulated-data analytics (moderate): Healthcare is only about 5% of the local sample, but Washington's My Health My Data Act increases the value of privacy-aware analytics for consumer health and health-data products.[17][38]

Where to focus: Prioritize enterprise or large-company teams where analytics directly supports operations, products, or AI deployment, and treat pure report-only analyst jobs as secondary targets.

Skills and Credentials Worth Pursuing

• Python (table stakes): Python appeared in about 60% of sampled local postings, making it the clearest screening skill across analyst, scientist, and AI-leaning roles.[12]
• SQL (table stakes): SQL showed up in about 45% of local postings and remains core even as AI tools automate parts of query writing.[12][15]
• Machine learning (differentiator): Machine learning was requested in about 40% of sampled local postings, which tells you Seattle demand leans beyond BI into predictive and AI work.[12]
• Power BI / PL-300 (differentiator): Data visualization appears in about 15% of local postings, certifications are rarely required, and the Microsoft Certified: Power BI Data Analyst Associate offers a practical way to prove BI fluency when you lack direct experience.[12][39][20]
• Workflow automation and BigQuery-style querying (differentiator): National salary analysis says analysts who can query BigQuery or automate workflows with Python earn more than Excel-only analysts, and that maps well to Seattle's heavy Python and SQL demand.[16][12]
• MLOps, data engineering, and cloud deployment (premium): Production-ready AI is now a standard expectation, and MLOps, data engineering, and cloud skills are in high demand for data science and AI professionals in 2026.[18]
• Prompt engineering and LLM application design (differentiator): As generative AI becomes mainstream, prompt engineering is increasingly useful for teams building internal tools and AI-assisted analytics.[18]
• Privacy-aware analytics for consumer health data (premium): Washington's My Health My Data Act extends rules around consumer health data outside HIPAA, which can make privacy-aware analytics more valuable in health and consumer data settings.[38]

Adjacent Roles to Consider

• Market Research Analyst (bridge): It uses similar data storytelling, survey interpretation, dashboard, and stakeholder skills but sits closer to marketing teams than core data platforms.
• Revenue Operations Analyst (both): It rewards SQL, reporting, forecasting, and process improvement while shifting you toward GTM systems and business operations.
• Product Operations Analyst (bridge): It fits candidates who are strong in KPI design, experimentation support, and cross-functional communication but do not want a pure ML path.
• FP&A Analyst (pivot): It lets analytically strong candidates move into forecasting, scenario modeling, and executive decision support.

30 / 60 / 90-Day Plan

First 30 Days

• Rewrite your resume into two versions: one for business analytics/BI and one for data science or AI delivery, because Seattle openings are split by seniority and scope, not just title.
• Audit your search filters now. If you are remote-only, you are ignoring most of the local market because about 60% of sampled roles were on-site and about 30% were hybrid.[14]
• Build one portfolio piece that starts with a business question, shows SQL plus Python work, and ends with a decision memo. Do not submit notebook-only work.
• Create a target list of 30-40 employers that includes long-tail enterprise and applied-product teams, not just the best-known tech brands, because local hiring is fragmented across more than 175 companies.[22][33]

Days 31-60

• Add one production-minded project: scheduled pipeline, automated reporting, experiment readout, or model monitoring. Seattle demand is rewarding candidates who can move from analysis to usable workflow.
• Earn one signal credential only if it fills a gap. PL-300 is useful if your BI proof is weak; skip extra certificates if you already have direct experience.[20]
• Reach out to hiring managers and senior ICs in enterprise analytics, robotics, healthcare data, and business services teams instead of only recruiters.
• Prepare a local-compensation narrative using a realistic band anchored around current Seattle postings rather than national medians.[11]

Days 61-90

• If interviews are weak, narrow your lane: either become the strong BI-plus-automation candidate or the strong applied-ML candidate. The market is rewarding specificity.
• Add a domain angle to your profile—consumer health privacy, operations analytics, GTM analytics, or product experimentation—so you are not competing as a generic 'data person.'
• Broaden to adjacent roles if needed, especially Market Research Analyst, Revenue Operations Analyst, Product Operations Analyst, or FP&A Analyst.
• If you need sponsorship, prioritize employers with known immigration processes early and do not assume local roles will sponsor; only about 5% of postings with an explicit policy mention sponsorship availability.[34]

Methodology and Confidence

This April 2026 report was generated on May 11, 2026. Latest direct national data: April 2026. Latest direct Seattle-Tacoma-Bellevue, WA data: April 2026.

Confidence: Overall confidence: High. Based on 10 direct local occupation data points and 30 total local evidence items with recent coverage.

Limitations

• The hardest local labor data in this report lags the report month, so current conditions for Seattle data roles are inferred partly from later posting, salary, and layoff signals rather than a full official occupation read.
• This category combines analysts, data scientists, analytics engineers, ML and AI roles, statisticians, and operations-research-style jobs, so conditions can be stronger for senior AI work than for junior analyst work at the same time.
• Some local labor changes are preliminary and may be revised, especially smaller moves in labor force, employment, and unemployment.
• 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 posting totals or exact employer shares.
• Several pay figures here come from offered-salary samples or salary aggregators rather than a metro-wide government wage series for this exact category, so use them as range-setting inputs, not guarantees.

References

1. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-04 · data.bls.gov
2. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-04 · data.bls.gov
3. Reveliolabs. Job Openings - Revelio Public Labor Statistics (RPLS) · 2026-04 · reveliolabs.com
4. Reveliolabs. Employment - Revelio Public Labor Statistics (RPLS) · 2026-04 · reveliolabs.com
5. Federal Reserve Economic Data. Consumer Price Index for All Urban Consumers: All Items in U.S. City Average · 2026-03 · fred.stlouisfed.org
6. Federal Reserve Economic Data. Federal Funds Effective Rate · 2026-04 · fred.stlouisfed.org
7. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-03 · data.bls.gov
8. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-03 · data.bls.gov
9. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-03 · data.bls.gov
10. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
11. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
12. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
13. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
14. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
15. Kissmetrics. Will AI Replace Data Analysts? What the 2026 Landscape Actually Shows · 2026-03 · kissmetrics.io
16. Dev. Data Analyst Salary in 2026: The Complete Pay Guide (US & India) · 2026-01 · dev.to
17. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
18. Refontelearning. Refonte Learning : Data Science & AI in 2026: Top Trends, Essential Skills, and Career Strategies · 2026-03 · refontelearning.com
19. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
20. Dataquest. 12 Best Data Analytics Certifications in 2026 · 2025-12 · dataquest.io
21. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-03 · data.bls.gov
22. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
23. Cdn. Cdn - warn_notice_layoff · 2025-10 · cdn.geekwire.com
24. Warntracker. T-Mobile Lays Off 1,722 Workers — 3 locations WARN Notice April 2026 · 2026-02 · warntracker.com
25. Komonews. Oracle latest tech giant to announce Washington layoffs amid broader wave of regional cut · 2026-03 · komonews.com
26. Fox13seattle. Meta plans to lay off 168 workers in WA starting in May · 2026-03 · fox13seattle.com
27. Komonews. Komonews - warn_notice_layoff · 2026-01 · komonews.com
28. Warntracker. Wescom Financial Lays Off 72 Workers — 18 locations WARN Notice April 2026 · 2026-04 · warntracker.com
29. Federal Reserve Economic Data. Job Openings: Total Nonfarm · 2026-03 · fred.stlouisfed.org
30. Federal Reserve Economic Data. Hires: Total Nonfarm · 2026-03 · fred.stlouisfed.org
31. Federal Reserve Economic Data. Layoffs and Discharges: Total Nonfarm · 2026-03 · fred.stlouisfed.org
32. Reveliolabs. Mass-layoff Notices - Revelio Public Labor Statistics (RPLS) · 2026-04 · reveliolabs.com
33. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
34. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
35. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
36. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
37. Bureau of Labor Statistics. Bureau of Labor Statistics Data · 2026-03 · data.bls.gov
38. Accountablehq. Privacy Laws in Washington State (2026): Consumer, Health Data, and Recording Rights Explained · 2025-07 · accountablehq.com
39. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
40. Callings.ai. Callings.ai job-market aggregation · 2026-04 · callings.ai
41. Built In. 2026 Data Scientist Salary in Seattle, WA | Built In · 2026-05 · builtin.com
42. Reveliolabs. Salaries - Revelio Public Labor Statistics (RPLS) · 2026-04 · reveliolabs.com