Data Engineering Salaries in Chicago, IL

What is

Data Engineering

?

Data Engineering focuses on designing, building, and maintaining the infrastructure for collecting, storing, and analyzing data. Data Engineers ensure data is accessible and reliable for analysis. Their work supports data-driven decision-making.


Data engineering professionals develop data pipelines, optimize data storage solutions, and work with data scientists to implement data-driven solutions.

Common titles in 
Data Engineering
  • Analytics Engineer
  • Data Engineer
  • Senior Data Engineer
  • Data Operations Analyst
Salary range for 
Data Engineering
 (
P3
)
 in 
Chicago, IL
Interested in salary information for other levels?

Decoding job levels: What is a 

P3

?

Pave’s job levels are denoted by their track (P for Professional, M for Management) and their hierarchical level, as denoted by a number. The higher the number, the more senior the role. There are 10 individual levels, broken down as:

Management: M3, M4, M5 & M6
Professional:
P1, P2, P3, P4, P5 & P6

For a full explanation of Pave’s approach to levels visit our FAQ.

Salary comparison for 
Data Engineering
 (
P3
)
 by city

Want to compare salaries across different cities? Here are the average salaries for Data Engineering in major metros across the United States. Ready to view additional percentiles or Data Engineering levels?

Chicago, IL
$
130000
$
142700
$
155800
San Francisco, CA
$
145000
$
165000
$
189000
New York, NY
$
140000
$
155000
$
180000
P10
104081
P25
130000
P40
136797
P50
142700
P60
147000
P75
155800
P90
166945
Interested in more salary insights like equity comp or international benchmarks? Book a demo with our team -->
(function (h, o, t, j, a, r) { h.hj = h.hj || function () { (h.hj.q = h.hj.q || []).push(arguments) }; h._hjSettings = { hjid: 2412860, hjsv: 6 }; a = o.getElementsByTagName('head')[0]; r = o.createElement('script'); r.async = 1; r.src = t + h._hjSettings.hjid + j + h._hjSettings.hjsv; a.appendChild(r); })(window, document, 'https://static.hotjar.com/c/hotjar-', '.js?sv='); !function () { var analytics = window.analytics = window.analytics || []; if (!analytics.initialize) if (analytics.invoked) window.console && console.error && console.error("Segment snippet included twice."); else { analytics.invoked = !0; analytics.methods = ["trackSubmit", "trackClick", "trackLink", "trackForm", "pageview", "identify", "reset", "group", "track", "ready", "alias", "debug", "page", "once", "off", "on", "addSourceMiddleware", "addIntegrationMiddleware", "setAnonymousId", "addDestinationMiddleware"]; analytics.factory = function (e) { return function () { var t = Array.prototype.slice.call(arguments); t.unshift(e); analytics.push(t); return analytics } }; for (var e = 0; e < analytics.methods.length; e++) { var key = analytics.methods[e]; analytics[key] = analytics.factory(key) } analytics.load = function (key, e) { var t = document.createElement("script"); t.type = "text/javascript"; t.async = !0; t.src = "https://cdn.segment.com/analytics.js/v1/" + key + "/analytics.min.js"; var n = document.getElementsByTagName("script")[0]; n.parentNode.insertBefore(t, n); analytics._loadOptions = e }; analytics.SNIPPET_VERSION = "4.13.1"; analytics.load("0KGQyN5tZ344emH53H3kxq9XcOO1bKKw"); analytics.page(); } }(); $(document).ready(function () { $('[data-analytics]').on('click', function (e) { var properties var event = $(this).attr('data-analytics') $.each(this.attributes, function (_, attribute) { if (attribute.name.startsWith('data-property-')) { if (!properties) properties = {} var property = attribute.name.split('data-property-')[1] properties[property] = attribute.value } }) analytics.track(event, properties) }) }); var isMobile = /iPhone|iPad|iPod|Android/i.test(navigator.userAgent); if (isMobile) { var dropdown = document.querySelectorAll('.navbar__dropdown'); for (var i = 0; i < dropdown.length; i++) { dropdown[i].addEventListener('click', function(e) { e.stopPropagation(); this.classList.toggle('w--open'); }); } }