Data Engineer Salary New York 2026: Finance, FAANG & What the Market Actually Pays

New York City has two distinct technology labor markets, and data engineers sit at the intersection of both. Wall Street firms have been hiring quantitative data infrastructure talent for decades. Silicon Alley — New York's tech corridor centered around the Flatiron District and Hudson Yards — has grown into a genuine competitor, with offices from Google, Meta, Amazon, and Stripe now anchoring the market alongside fintech unicorns and hedge funds. The result is unusually strong demand and some of the highest data engineering salaries in the United States.

This guide covers what data engineers actually earn in New York in 2026, which employers pay the most, how the finance and tech sectors differ in compensation structure, and what NYC's tax environment means for your take-home pay.

New York Data Engineer Salary by Level (2026)

These ranges reflect the full NYC market across both finance and tech employers. The spread at senior and staff levels is wider than in most cities because finance firms and tech companies use fundamentally different compensation structures. For a broader view of data engineering compensation globally, see our Data Engineer Salary Guide 2026.

NYC's Dual Market: Finance vs. Tech

New York is the only major tech hub in the United States where financial services firms compete directly with Big Tech for data engineering talent at scale. Understanding which market you are entering changes your total compensation expectations significantly.

Finance (Goldman Sachs, JPMorgan, Bloomberg, Two Sigma, Citadel)

Wall Street's data engineering demand is driven by quantitative trading infrastructure, regulatory data pipelines, risk management systems, and machine learning platforms. These roles require the reliability and latency characteristics that consumer tech companies rarely need.

Goldman Sachs, JPMorgan, and Bloomberg pay data engineers competitively at base level — typically $130K–$165K for senior roles — but the real differentiation is the bonus. Finance bonuses at bulge-bracket banks and hedge funds range from 20% to 60% of base salary for data engineers on critical infrastructure teams. Two Sigma and Citadel sit at the upper end: senior data engineers can see total compensation of $280K–$380K when bonuses are included.

Cash compensation is the defining feature of finance data engineering. Bonuses are paid annually in cash, not in equity that may or may not vest. For engineers who want predictable, liquid compensation, finance is structurally advantageous.

Tech (Google, Meta, Amazon, Stripe)

New York's tech sector is anchored by FAANG offices and growing fintech companies. Google's New York office focuses on advertising, search infrastructure, and cloud sales engineering. Meta's New York presence spans data infrastructure, privacy engineering, and growth. Amazon has a significant AWS commercial and engineering presence. Stripe, with a large New York engineering hub, focuses on payments infrastructure.

Tech compensation is equity-heavy. A senior data engineer at Google New York earns roughly $160K–$195K in base salary, with RSU grants bringing annual total comp to $280K–$350K at L6. The equity upside at pre-IPO fintech companies like Ramp and Brex can be considerably larger — but illiquid until a liquidity event.

Stack Premiums: Tools That Move the Number

Specific technical skills command measurable salary premiums in New York:

Kafka expertise carries the highest premium in New York specifically because of the finance sector's demand for real-time streaming infrastructure. Engineers who can build and operate high-throughput Kafka clusters for trading or payments workloads are genuinely scarce.

NYC vs. SF vs. Seattle vs. Remote

New York's combined state and city income tax is among the highest in the United States. New York State levies up to 6.85% on income above $107K, and New York City adds a further 3.876%. Combined, a senior data engineer earning $160K pays roughly $17K per year in state and city taxes alone — meaningfully more than Seattle at zero and significantly more than Texas or Florida-based remote engineers.

This does not make New York a bad market. Base salaries are high, and the finance sector's cash bonus structure can still produce exceptional take-home pay even after taxes. But the sticker salary is not the full story, and comparing New York offers to Seattle or remote offers requires the full tax calculation.

NYC Salary Transparency Law

New York City's salary transparency law, effective since late 2022, requires employers to post salary or pay range information in all job listings. For data engineers, this means you can see the intended pay band before applying — and use it as a floor in negotiations rather than a ceiling.

Finance firms often post wide ranges to preserve negotiating flexibility. Tech companies tend to post narrower, more accurate bands. Either way, the law gives candidates more information than most other markets, and well-prepared engineers should use posted ranges as a starting point for discussing total comp, not just base.

Finance vs. Tech: Which Structure Works for You

The choice between finance and tech data engineering in New York is ultimately a question of compensation structure preference:

Finance pays in cash. Annual bonuses are liquid from day one. There is no cliff, no vesting schedule, and no dependence on a company's stock price. The tradeoff is that bonuses can be cut in down years, and total comp ceiling is lower than FAANG equity in a strong stock market.

Tech pays in equity. RSU grants at Google, Meta, and Amazon can produce exceptional total comp in good market years, with L6 engineers regularly clearing $300K–$350K. The tradeoff is a four-year vesting schedule, exposure to stock price volatility, and the fact that the most valuable grants are front-loaded in the initial offer.

For data engineers early in their career, tech companies tend to offer stronger growth paths and more structured mentorship. For those with 7+ years of experience and specialized finance-adjacent skills, the hedge fund path (Two Sigma, Citadel, Jane Street) can produce the highest total compensation of any employer in the city.

Negotiating in New York

New York's dual-market structure gives experienced data engineers genuine leverage. A strong candidate with Kafka, Spark, and Snowflake expertise can generate competing offers from both finance firms and tech companies — which creates real negotiating power when the compensation structures differ substantially.

Finance firms negotiate cash. If you want a higher base or a larger bonus guarantee, that conversation is possible — especially with a competing tech offer showing meaningful equity upside. Banks will sometimes convert equity value to a cash equivalent to close a candidate.

Tech companies negotiate equity. Larger RSU grants, accelerated vesting, and sign-on bonuses paid in stock are the primary levers. If you have a competing finance offer with a strong cash bonus, use it to push for a larger initial RSU grant.

Level correctly at FAANG. At Google and Meta, a single level difference represents $40K–$80K in annual total comp. Push back before accepting if your experience supports the higher level.

For context on what remote-first data engineering roles pay compared to New York, see our Remote Data Engineer Salary Guide 2026. For European comparison, see our Data Engineer Salary Berlin 2026 guide. For broader tech salary context across New York City, see Tech Salaries New York 2026.

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Related Salary Guides

Data Engineer Salary Guide 2026 — global comparison across six cities

Remote Data Engineer Salary 2026 — remote-specific comp analysis

Data Engineer Salary Berlin 2026 — European market comparison

Tech Salaries New York 2026 — cross-role NYC compensation benchmarks

New York in 2026 rewards data engineers who understand its dual-market structure. Finance pays in cash; tech pays in equity. Both can produce exceptional total compensation for engineers with the right stack and the willingness to negotiate across both markets simultaneously.