Data Engineering Salaries in New York, NY

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 
New York, NY
Interested in salary information for other levels?

Sign up and connect your HRIS for full access -->

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?

‍

Connect your HRIS system to Pave -->

25th percentile
50th percentile
75th percentile
Chicago, IL
$
130000
$
142700
$
155800
San Francisco, CA
$
145000
$
165000
$
189000
New York, NY
$
140000
$
155000
$
180000
P10
115500
P25
140000
P40
150000
P50
155000
P60
165000
P75
180000
P90
202500
Interested in more salary insights like equity comp or international benchmarks? Book a demo with our team -->

Browse other jobs in 

New York, NY

Accounting
Business Development
Business Operations
Customer Success
Customer Support
Data Analyst
Data Science
Enterprise Account Services
Executive Assistance
Machine Learning
Product Design
Product Management
Product Marketing
Project/Program Management
Sales
Software Engineering
Strategic Planning
Solutions
Market DataCompensation WorkflowsCommunication
Product
BenchmarkingMarket PricingCompensation PlanningTotal RewardsVisual Offer Letter
Resources
BlogPave Data LabWebinarsCase StudiesGuidesHelp CenterIntegrations
Company
PricingCustomersAboutCareersSecurity & PrivacyPress RoomContact Support
Legal
Privacy PolicyTerms of UseService Terms
Fulfillment PolicyMaster Subscription Agreement
Built in San Francisco, CA
© Copyright 2023

Pave's successful SOC 2 Type 2 report was issued by the Johanson Group and is actively monitored by Drata.

(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'); }); } }