Data Analyst Salary Guide 2026

Last updated: May 12, 2026 | By Mike Carter

Data analysts in the United States earn $72,000 to $98,000 in base pay in 2026, with senior analysts and analytics leads pulling $110,000 to $145,000 once domain specialization, SQL depth, and cloud BI skills enter the picture.

That range is the headline. The reason it spreads $73K wide on a single title is that nobody, including the salary aggregators, actually agrees on what a data analyst is. Some companies file an Excel-and-Power-BI generalist under that title. Some file a near-data-engineer with dbt and Snowflake under the same one. The market is paying the second person almost double what it pays the first, and most hiring managers don’t notice until the third or fourth candidate walks because the offer was scoped to the wrong tier.

I’m Mike Carter. I spent twenty years scaling consumer brands through their growth windows. Electric Visual from the founding team out. Skullcandy through IPO. ComplexCon. FUEL TV+ relaunched across a hundred countries and seven hundred million devices. Every one of those companies miscalibrated their analytics hires the same way at some point: either over-scoped and underpaid, with the role pulling double duty as a near-data-engineer on a generalist budget, or under-scoped and overpaid, where the offer was set against a senior band for what turned out to be Excel-and-Power-BI work. I now run high-growth workforce searches at KORE1. This guide is the comp framework I wish I’d had at Skullcandy in 2009.

Bias on the table. KORE1 collects a fee when we place a candidate, including through our data analytics staffing practice. The numbers below come from public salary sources, the BLS, and our internal placement data. I’ll flag where the sources disagree.

What Six Salary Sources Say a Data Analyst Earns in 2026

No single salary platform gets this right. They are not even trying to measure the same thing. Glassdoor pulls self-reported total pay from people who chose to file. ZipRecruiter scrapes active listings. Indeed averages from posted ranges going back roughly three years, which means some of their numbers are dragging 2023 budgets into a 2026 reading. PayScale survey respondents skew early-career. Salary.com models. Built In leans tech-heavy and metro-weighted.

So you need the composite. Here is what six independent sources report as of early 2026, plus where KORE1 actually closes offers.

Three things stand out.

The PayScale floor sits roughly $15,000 below every other source, which is not a methodology error so much as a population sampling story rooted in who actually fills out their salary tool, which skews heavily toward early-career professionals and toward people who suspect they’re underpaid and went looking for confirmation. Take it as a soft floor, not a market read.

Built In’s $92,000 number runs the highest because the platform is metro-tech-weighted, with a sample that pulls disproportionately from venture-backed companies in SF, Seattle, New York, and Boston, so if your search sits outside those metros or runs against a non-tech employer, the Built In benchmark will overshoot the close-rate market by a meaningful margin. If you are hiring outside those metros at a non-tech employer, Built In is going to overstate your benchmark.

KORE1’s placed-base number is the cleanest apples-to-apples read for the midmarket, where most of these hires happen. It sits roughly $5,000 above ZipRecruiter, $4,000 above Salary.com, and $2,000 above the Glassdoor median. That gap is real. Sticker prices on listings underprice the close-rate market by about 6%. Companies that anchor on the posted range and refuse to budge tend to lose searches in the third week.

For the federal anchor, the Bureau of Labor Statistics classifies most data analyst work under Operations Research Analysts (15-2031). May 2024 median annual wage: $87,640. Projected growth 2023 to 2033: 23%, which BLS labels “much faster than average.” The 10th percentile reports $53,650 and the 90th percentile clears $171,710, which roughly bookends the range we see in real searches.

Data Analyst Salary by Experience Level

Title is noise. Experience moves the number.

Composite of Glassdoor, ZipRecruiter, Indeed, Salary.com, PayScale, Built In, and KORE1 placement data, Q1 2026.

Entry-level is doing a thing I have never seen this loudly before. The floor moved up about $4,000 from 2024, and at the same time the bar to enter moved up even more. Companies that two years ago would have hired a junior analyst with a coursework portfolio now want at least one internship plus a public GitHub or a published Kaggle notebook plus a real recommendation. The jobs aren’t easier to get. The pay is just slightly better for the people who get them.

Mid-level is the war zone tier for this role. Four years of SQL, dashboarding experience in either Tableau or Power BI, ideally one stretch where the analyst owned the metric for a function, not just produced the chart. Those candidates routinely interview at three companies simultaneously. Our average mid-level data analyst search closes in 18 days, and that is fast because we run a tight outbound process; the typical in-house search at a midmarket company runs 6 to 9 weeks at this tier, and the gap between weeks 4 and 9 is where most of the strong candidates accept somewhere else.

Senior is where titles get weird. We placed a senior data analyst into a Series C fintech in Costa Mesa last quarter at $138,000 base. Same week, we placed another senior data analyst at a 600-person logistics company in Dallas at $108,000 base. Same title. Same listed years of experience. The Costa Mesa person was doing dbt model design, Snowflake performance tuning, and writing the eval criteria for executive dashboards. The Dallas one was producing weekly reports out of Excel and a SQL Server warehouse. Both companies called the role “Senior Data Analyst.” Only one was paying the analytics engineering market rate that the work actually required.

Lead and Principal are mostly title-stretched senior ICs. Companies use the title to retain people who don’t want to manage, which is fine, but the comp band needs to reflect that the role is doing principal-engineer-equivalent technical work without the engineering payband. We have lost candidates to engineering manager roles paying $190,000 because the company didn’t realize their lead analyst was being recruited against the engineering ladder.

Data Analyst Salary by City

Geography still moves the number, even after the remote-flattening narrative of 2021 turned out to be half a story. Cost of living in the top metros pushes salaries up regardless of company budget, and the analytics-heavy industries cluster more than they used to, not less.

Glassdoor metro filters, March 2026; Built In metro reports; KORE1 internal placement data.

The Bay Area gap to the rest of the country narrowed a little. Not because the Bay went down but because the second tier of metros went up faster. Austin, Denver, and Atlanta added $8,000 to $12,000 to median base for this role in the past 24 months. The big anchor employers in those metros figured out that hiring a senior analyst at a Texas or Colorado salary was a temporary discount, and that discount has mostly closed for the mid-to-senior tiers.

Orange County is a quiet outlier we see often because that’s where KORE1 is headquartered. The Irvine, Costa Mesa, and Newport Beach corridor pays close to LA county rates for senior analytics roles because of the dense cluster of consumer goods, gaming, SaaS, and direct-to-consumer commerce companies in that submarket competing for the same analytics talent that historically would have priced against the LA market. Hiring managers based in OC who anchor to “Los Angeles” comp data often misprice the role 8 to 10% high because LA Metro Glassdoor data is dragged up by entertainment and tech employers headquartered closer to Culver City, Santa Monica, and the West Side, none of which is the OC submarket reality.

Data Analyst, Data Engineer, Business Analyst: Where the Lines Actually Are

This question kills more searches than any other. A company posts a Data Analyst role, writes the JD with skills closer to a Data Engineer, sets the comp at Business Analyst rates, and then wonders why nobody good is applying. Three jobs. Three pay bands. Different talent pools.

Analytics Engineer is the title most often blurred into Data Analyst. If your role description includes dbt, Snowflake performance work, semantic layer ownership, and any kind of pipeline-adjacent responsibility that would have lived under a Data Engineer five years ago, you are hiring an Analytics Engineer and paying $30,000 below market. Renaming the role costs nothing and opens the candidate pool by maybe 3x.

Business Analyst overlap goes the other direction. If your “data analyst” is mostly running requirements workshops, writing Confluence pages, and producing pretty quarterly review decks, that’s a BA role, and you’ll attract a different (often stronger) candidate pool by calling it what it is. Our business analyst staffing team sees this title misalignment in roughly a third of intake calls.

Skills That Move the Number

Some analyst skills are oxygen. Having them gets you in the door. They don’t pay more. The premium skills are the ones that change which talent pool you are in.

• SQL depth, not just SQL exposure. Everyone writes SELECT statements. The premium is for analysts who write window functions correctly the first time, understand query plans, and can rewrite a 90-second query down to 6 seconds without breaking the output. That skill alone adds $8,000 to $12,000 to mid-level offers in our experience. SQL shows up in nearly every data analyst posting we see, and the depth gap among candidates is wider than any other technical area.
• Python for analytics, not just for ML. Pandas, NumPy, occasionally PySpark. The analyst who can pull data, clean it, run a basic statistical test, and visualize the result without leaving Jupyter is a different hire than one who lives in Power BI.
• dbt and the modern analytics stack. This is the new dividing line. Analysts with hands-on dbt experience, comfort with Snowflake or BigQuery, and a working understanding of semantic layers (Looker, Cube, dbt Semantic Layer) are routinely fielding $115,000 to $135,000 offers at the mid-level. Companies post these as Data Analyst roles. The market prices them as Analytics Engineers. You see the problem.
• Tableau and Power BI, both. Most senior analyst postings now want documented comfort in at least one and meaningful exposure to the other, with the bar at the senior level moving toward “I have rebuilt a production dashboard environment from scratch in this tool” rather than “I have used it.” Tableau still owns the high end of the enterprise BI market. Power BI dominates Microsoft-heavy organizations, which is most of the Fortune 1000. Looker fits the modern data stack crowd. Specialization helps; total avoidance of any of the three is a problem.
• Domain depth. The fastest pay bump I have seen in the last two years is not technical at all. It is industry expertise. A senior analyst with 4 years in healthcare analytics, who understands HIPAA, claims data, and the difference between MIPS and HEDIS, can name a $30,000 premium over a same-skilled generalist. Same for fintech, ad-tech, and supply chain. Domain knowledge converts into dollars faster than learning another BI tool will.
• Statistical literacy that goes past “I took a stats class once.” A/B testing methodology that holds up under pressure, comfort with confidence intervals, knowing when a correlation is going to embarrass the team. This is the analyst who gets pulled into the executive review, not the one assembling the deck for it.

If you are a candidate reading this section, the leverage isn’t to stack five skills shallow. Pick two and go deep. Senior analysts who can credibly say “I am the dbt person at my company” or “I built the experimentation framework for our growth team” close offers $15,000 to $25,000 above generalist peers with the same nominal years of experience, every single time, because the specificity converts into both a sharper interview narrative and a cleaner reference call.

Industry Sets the Pay More Than Most People Think

Same title. Same skills. Different paycheck. Industry is the second-biggest variable after experience.

Glassdoor industry-level data and Built In, March 2026.

The tech-to-non-tech gap is the part most hiring managers underestimate. A senior analyst leaving a fintech for a 400-person manufacturer is typically taking a $15,000 to $25,000 cut on base, which is fine if the work and the life are the right trade for the person, and most of the candidates who make that move are deliberately choosing slower pace and less travel over peak comp. The trap is the company that wants to recruit out of fintech but pay manufacturing rates without acknowledging the comp delta or offering anything to bridge it, like equity, sign-on, or an honest title bump. That doesn’t work, even when the candidate is open to the move. The other offers on the table are too easy to find.

Remote, Hybrid, and Onsite

The 2021 prediction that remote work would flatten all salaries across geographies has not held up. Three years in, the data is more complicated.

Fully-remote data analyst postings are a smaller slice of the market than people assume. The percentage that does not require any in-office presence sits around 12 to 15% of new postings in our search board, down from a 2022 peak of 30%+. Hybrid is the dominant model, typically two or three days in office. Most of the salary data above assumes hybrid as the baseline.

For remote roles, two patterns. Companies that location-adjust often anchor to a national base near $75,000 to $90,000 for mid-level and avoid the metro premiums entirely. Companies that don’t location-adjust either pay the high-metro rate to everyone (rare, mostly venture-backed tech with strong revenue) or pay a national rate and lose to companies that pay metro rates to the candidates in expensive cities. Watch the math on both sides.

If you are a hiring manager assuming “remote means we save money on comp,” that is the wrong starting point in 2026. Strong candidates still know the local market they’re sitting in. A senior analyst in Brooklyn knows what New York pays. The salary discount you can charge for remote, if any, is narrow.

Certifications: When They’re Worth the Money

Most senior hiring managers will tell you certifications don’t matter. The placement data says otherwise, at least for two specific roles in the analyst space.

Tableau Desktop Certified Associate and Microsoft Power BI Data Analyst Associate (PL-300) are both genuinely useful signals at the mid-level. Not because the cert itself proves competency, but because automated resume screening pulls these credentials as exact-match keywords, and the gap between “candidate has the cert” and “candidate has the same skill but no cert” is wider in initial screening rounds than the underlying competency gap actually justifies. If you have three years of dashboard experience and no cert, you are losing 30% of your inbound to an applicant tracking system filter you did not even know existed, and the candidates passing the filter often have less hands-on experience than the ones being silently rejected.

SnowPro Core (Snowflake) and dbt Analytics Engineering Certification are the two that actually move offer letters. Both are recent enough that the supply of certified analysts is thinner than demand, and companies hiring into the modern data stack are willing to pay a $5,000 to $10,000 premium for either. The Google Data Analytics Professional Certificate via Coursera helps for entry-level candidates with no degree; past the second job, it stops doing meaningful work for you.

I would skip the AWS Certified Data Analytics Specialty unless you are targeting AWS-shop employers specifically. The pass rate is brutal, the prep time is real, and the offer-letter lift is smaller than the Snowflake or dbt equivalents.

What KORE1 Sees in the Real Hiring Market

Three patterns from our placement data, none of which show up in salary aggregators.

First, the gap between posted compensation and accepted compensation widened meaningfully in 2025, with the spread between the top of the posted band and the actual signed offer averaging roughly $7,000 across our analytics placements last year. Companies post a range, the strong candidate finishes the loop, and the offer comes in at the top of the band, sometimes $5,000 to $10,000 above. We see this on roughly 40% of our analytics searches. The posted range is the marketing material; the close-rate is the real number. If you are benchmarking against listings on LinkedIn or Indeed, your numbers are probably 6% to 9% low.

Second, the time-to-hire compressed. Our average fill time on data analyst searches across IT was 17 days in 2025, with mid-level moving in the 14 to 21 day window and senior closing in 25 to 35 days from kickoff to signed offer, which is faster than the 24-day overall blended average we track across all KORE1 IT placements and noticeably faster than 2023 when the same searches were running 28 days at mid-level. KORE1’s overall 12-month retention on placements sits at 92%, which is the number we lead with when we negotiate a fee, and the analytics roles are running roughly in line with that average. The hiring teams that lose searches almost always lose them between offer extension and acceptance. The candidates we lose to a competing offer overwhelmingly accept the offer that landed first.

Third, counter-offers from current employers are back, and they’re aggressive. Roughly one in four mid-level offers we extended in Q4 2025 generated a counter from the candidate’s current employer, often inside 48 hours of the candidate giving notice, with the counter typically landing $8,000 to $14,000 above the offer the candidate was about to accept. The counter-acceptance rate is around 30%, which is high but not the 50%+ we saw briefly in 2022. The companies losing on counter are almost always the ones who took more than 3 weeks to extend after the final interview.

If you are weighing whether to use a staffing partner for an analyst hire, my honest view: most companies don’t need one. If your inbound is healthy, your interview loop is under 3 weeks, and your offer band is current, hire direct. Where we earn our fee is the role that has been open for 60+ days, the role with an unusual skill combination (think dbt plus healthcare claims plus Looker), or the situation where you need 3+ analysts on a deadline. Those are the searches where the math works for everyone. Our data analytics staffing team focuses on exactly those engagements.

Common Questions From Hiring Managers and Candidates

What is the average data analyst salary in 2026?

The composite median for U.S. data analysts in 2026 sits around $82,000 to $87,000 in base pay, with the realistic mid-level range $78,000 to $108,000.

Glassdoor shows $84,300. ZipRecruiter $79,200. Salary.com $82,400. Indeed $76,500. PayScale runs lower because the survey skews early-career. Built In runs higher because the sample skews tech and metro. KORE1’s placed-base median is $86,500, which sits in the middle of the public sources and is the cleanest read on what midmarket employers actually close offers at.

How much does an entry-level data analyst make?

$58,000 to $75,000 base in most U.S. metros, with the SF Bay, NYC, Seattle, and Boston tier adding another $5,000 to $15,000 on top of that floor.

The floor moved up about $4,000 from 2024, but so did the entry bar. A degree alone no longer does it for most postings. Strong applicants are showing up with at least one internship, a public portfolio of SQL or Python work, and a recommendation from a real manager.

Senior data analyst pay: what’s realistic?

$112,000 to $145,000 base for 6 to 9 years of experience, with senior IC analysts in tech metros clearing $150,000 once equity is layered in.

The number depends heavily on whether the role is doing analytics engineering work in disguise. If the JD includes dbt, Snowflake performance tuning, or pipeline-adjacent ownership, the market is paying that role $130,000 to $150,000 even when the title says “Senior Data Analyst.” Mispriced senior roles are the single most common reason analytics searches stall.

Do data analysts make more than business analysts?

Slightly less at the entry and mid levels, similar at senior, and it inverts past 10 years where business analysts often out-earn data analysts.

Business analyst mid-level base typically lands $85,000 to $110,000. Data analyst mid-level lands $78,000 to $108,000. Lots of overlap. Past senior, business analysts who move into program management or strategy roles often pull ahead. Data analysts who move toward analytics engineering or data science roll into different paybands entirely. See our business analyst salary guide for the full comparison.

Which city pays the most for data analysts?

San Francisco Bay Area at $108,000 to $135,000 median total pay, with senior IC analysts clearing $150,000 once equity is included.

Seattle and New York are next, each landing somewhere in the $92,000 to $118,000 band. The interesting move in the last two years has been the second-tier metros, especially Austin, Denver, and Atlanta, which closed the gap to the Bay by $8,000 to $12,000. The biggest unexpected payer in our data is the Irvine and Costa Mesa corridor in Orange County, which trails LA by very little because of the cluster of consumer goods, gaming, and SaaS companies in that submarket.

What skills increase data analyst salary the most?

Deep SQL, dbt and the modern analytics stack, statistical literacy, and domain expertise in healthcare, fintech, or supply chain. Combined, those four can move an offer $20,000 to $35,000 above generalist base.

SQL depth is the easiest place to start because it lifts every offer regardless of industry. dbt is the highest-leverage emerging skill because supply of certified analysts is still thinner than demand. And domain expertise often pays more than another BI tool would. We have placed senior healthcare analytics analysts at $30,000 premiums over same-titled generalists.

Is the data analyst role still in demand in 2026?

Yes. BLS projects 23% growth for Operations Research Analysts between 2023 and 2033, which it labels much faster than average. Demand for analysts with modern stack skills is running ahead of supply.

The role has shifted, though. The pure dashboard-and-report-builder analyst is being squeezed by self-service BI tools and by the analytics engineer role taking the technical end of the work. Demand is moving toward analysts who can do both the technical and the storytelling side, and that’s the cohort where comp is rising fastest.

Should I use a staffing partner to hire a data analyst?

Only for searches over 45 days open, unusual skill combinations, or team builds with a deadline. For straightforward mid-level direct hire analyst hires, most companies do better going direct.

If you are hiring a single mid-level analyst and your interview loop runs in under 3 weeks with a current comp band, you almost certainly don’t need us. Where we earn the fee is the niche search (think dbt plus claims plus Looker), the role that has been open all quarter, or the situation where you need to fill 3 to 5 analyst seats inside a tight window. Our recruiter team handles those engagements. For anything else, save the budget.

How to Use This Guide

Hiring managers: pull the experience-level table, adjust for your industry, then your metro, then verify against our salary benchmark assistant for your specific search. Build the offer band before you write the JD, not after.

Candidates: cross-reference at least three sources before anchoring to a number. If you have dbt, Snowflake, or domain depth in a specialized industry, you have more leverage than the salary aggregators suggest. The posted range is a starting point, not the ceiling.

And one last point that applies to both sides. The single fastest way to know whether the comp on the table is reasonable is to look at how fast the offer is moving. Strong candidates accept offers that arrive within a week of the final round, at the top of the posted band, with a clean role definition. If the negotiation drags 3 weeks, the comp is probably wrong, the scope is probably unclear, or both. That’s a Mike Carter pattern from twenty years of growth-stage hiring. The deal that stalls is the deal that breaks.