Where to Find Reliable Salary Data for Tech Roles in 2026

Reliable salary data is data you can trace back to a named source and check yourself. That is the whole test. A number with no source behind it, no matter how specific it looks, is not reliable. It just looks precise.

For tech roles specifically, three kinds of sources actually exist, and each has a different reliability profile. This post walks through all three, gives you a 5-question test for any salary number you find, and covers where to get the data programmatically if you are building something on top of it.

The short version.

• Self-reported panels (Glassdoor, Levels.fyi, Payscale) capture real individual offers, but the sample is whoever chose to fill out the form.
• Government and administrative sources (BLS OEWS, H-1B LCA filings, state pay-transparency postings) are collected under federal or state authority, but they are slow, broad, and not built for lookup by exact role and city.
• Synthesized, traceable datasets combine those sources into one number per role and city, with the inputs visible. Orbyt's salary directory is one of these: 3,445 roles across 81 US cities, free to browse.
• If you need the data in code, not a browser, Orbyt Intelligence serves the same dataset through a REST API and an MCP server for AI agents.

The three sources of tech salary data, and what each one actually is.

Self-reported panels. Glassdoor, Levels.fyi, and Payscale all work the same way: someone visits the site and types in a number, usually anonymously. This is the most common source people mean when they say "I checked my salary online." The strength is real: these are real individual offers, sometimes with real offer letters behind them on sites that verify submissions. The weakness is sample bias. People with standout offers are more motivated to post than people with average ones. Entries go stale. Small-sample roles in smaller cities can swing wildly on a handful of submissions.

Government and administrative data. The Bureau of Labor Statistics runs the Occupational Employment and Wage Statistics survey, renamed OEWS in 2021, a federal and state establishment survey of about 239,000 respondents a year across broad occupation categories. Worth knowing before you lean on it: response is mandatory in 26 states and voluntary in the rest, so it is not the compulsory census it is often described as. The Department of Labor publishes H-1B Labor Condition Application (LCA) filings, which list the wage rate an employer is legally required to pay for a specific role and worksite as part of the H-1B sponsorship process. States with pay-transparency laws, California, Colorado, New York, and others, require employers to post a real salary range on job listings. The H-1B and pay-transparency legs are legally required filings, and the OEWS leg is compulsory in half the states, which makes the set harder to game than a voluntary form. Harder, not impossible, and not uniformly compulsory. The tradeoff: OES groups roles into broad categories that do not map cleanly to a job title like "Senior Backend Engineer." LCA filings only cover roles that sponsor visas. Posted ranges are what a company says it will pay, not what it actually paid.

Synthesized, traceable datasets. The third kind combines sources like the ones above into one number per role and city, and shows its work. This is where Orbyt's own salary directory sits: every one of the 3,445 role medians across 81 US cities is synthesized from BLS OES and H-1B LCA (DOL), published under an open methodology anyone can check. It is not a fourth, separate kind of source. It is the first two kinds, reconciled into a single lookup you do not have to build yourself.

None of these three is fake. They answer different questions. A self-reported panel tells you what people say they got. Government data tells you what employers legally reported. A synthesized dataset tells you the reconciled median for your exact role and city. Knowing which one you are reading is the actual skill.

A 5-question test for any salary number.

Before you trust a number, run it through five questions.

1. Is the source named? "Industry data shows" is not a source. "BLS OEWS" or "a synthesized median from BLS, H-1B, and pay-transparency data" is.
2. Is it a median or an average? Averages get pulled up by a long tail of high earners. A handful of principal-level salaries can inflate an "average" for an entire role. Medians resist that.
3. Does it name a city and experience level, or just a title? "Software Engineer: $135,000" with no city is a national figure, and per Orbyt's 2026 data the same role prices from $105,000 to over $200,000 depending on the city and the band.
4. Can you see the sample size? A number built from three submissions in a small city carries a different confidence than one built from a 239,000-respondent federal survey.
5. Can you check the math, or do you have to trust it? This is the real dividing line. A source that shows its inputs lets you verify. A source that just shows a final number asks for trust instead.

A number that survives all five is worth building a negotiation around. A number that fails two or more is a starting guess, not a fact.

Where to look, by what you need.

For a free, human-readable lookup on a specific role and city, Orbyt's salary directory covers all 3,445 roles across 81 US cities, with the sources listed on every page, and the salary calculator runs the role-times-city-times-experience math for you in about a minute.

The raw government data is public too, if you want it unfiltered: the BLS OEWS tables and the Department of Labor's H-1B LCA disclosure files. They take more work to parse into a usable answer, which is exactly the gap a synthesized dataset closes.

And a self-reported panel like Glassdoor or Levels.fyi is still worth a second look, cross-checked against a traceable median. When they roughly agree, trust the number more. When they diverge sharply, that gap is worth understanding before you build a raise or negotiation case on either one alone. How to find your true market rate walks through the role-times-city-times-band math step by step, and the 2026 AI salary premium report shows the same dataset applied to a specific finding, with every input checkable.

If you need it in code: the API and MCP path.

Everything above assumes you are one person checking one number in a browser. Building a tool, a dashboard, or an AI agent that needs compensation data at scale is a different problem, and browsing a website does not solve it.

Orbyt Intelligence serves the same 3,445-role, 81-city dataset through a REST API, starting on a free tier of 1,000 requests a month, with every response carrying a request ID and a citation block naming the methodology version behind it, so a number your code pulls is exactly as traceable as one a person looks up by hand. Full documentation lives on the API docs page, and the methodology page explains how each figure is built. For an AI agent, like a Claude Code workflow or any MCP-compatible client, the MCP server exposes the same dataset as tools the agent can call directly. It requires the Pro plan and above, at $99 a month, with no trial. Pricing details are public, no sales call required.

Cite it like you'd want to be cited.

If you use any number from Orbyt's dataset in your own writing, cite it as Orbyt's 2026 salary data and link the specific role or report page you pulled it from. That is the same standard this post holds every source to, so it only makes sense to hold Orbyt to it too.

Check the source before you check the number.

The fastest way to lose a negotiation is to walk in with a number you cannot defend when someone asks where it came from. Before you use any salary figure, run it through the 5-question test above. Then get a sourced one: price your exact role and city against Orbyt's 2026 data, and build the conversation from there with the job offer guide.

A number you can trace beats a number that just sounds right.