Data Engineer Salary Overview
The Data Engineer is one of the most important roles in the Technology sector of the US economy in 2026. With a median annual salary of $118,600, compensation for this position ranges from $69,800 at the entry level to $192,000 for highly experienced professionals in top-paying markets.
This career typically requires Bachelor's or Master's in Computer Science, Data Engineering, or Information Systems. Valued professional credentials include AWS Data Analytics Specialty, Google Professional Data Engineer, Databricks Certified Data Engineer, Snowflake SnowPro Core. On a day-to-day basis, professionals in this role focus on building ETL/ELT data pipelines, designing data warehouse architectures, implementing real-time streaming systems, ensuring data quality and governance, optimizing query performance, managing data lake infrastructure, and creating self-service analytics platforms.
The job market for this position shows 28% from 2022-2032 as organizations build modern data platforms and real-time analytics infrastructure growth, with demand strongest in specializations including real-time streaming (Kafka/Flink), data lake architecture, data mesh implementation, analytics engineering (dbt), and ML feature engineering. AI creates more data to process and manage; data engineers who build AI-ready data infrastructure are commanding top-tier compensation
Salary Range: The typical Data Engineer in the US earns between $69,800 and $192,000 per year, with a median of $118,600.
What Does a Data Engineer Do?
A Data Engineer spends their workday building ETL/ELT data pipelines, designing data warehouse architectures, implementing real-time streaming systems, ensuring data quality and governance, optimizing query performance, managing data lake infrastructure, and creating self-service analytics platforms. The role requires proficiency with industry-standard tools and technologies including Python, SQL, Apache Spark, Airflow, Kafka, dbt, Snowflake/Databricks/BigQuery, Terraform, Docker, data pipeline orchestration tools.
The typical work environment involves data platform teams in tech companies or enterprises; collaborative work with data scientists, analysts, and business stakeholders. Within the profession, you can specialize in areas such as real-time streaming (Kafka/Flink), data lake architecture, data mesh implementation, analytics engineering (dbt), and ML feature engineering, each requiring different skill sets and offering different compensation levels.
Day-to-day responsibilities vary based on seniority and organization size. Entry-level professionals often focus on execution tasks under supervision, while senior professionals take on strategic planning, mentoring, and cross-functional leadership.
Data Engineer Salary by Experience
Compensation for a Data Engineer increases substantially with experience. Entry-level professionals (0-2 years) typically earn around $81,834, while mid-career professionals (3-6 years) reach the median of $118,600. Senior professionals (7-12 years) earn approximately $164,854, and those in lead or principal roles can expect $167,226 or more.
The typical career progression follows this path: Junior Data Engineer → Data Engineer → Senior Data Engineer → Staff Data Engineer → Data Platform Manager → VP of Data Engineering. Each advancement typically requires 2-4 years and demonstrating increasing scope of responsibility.
| Level | Salary | Hourly | Take-Home |
|---|---|---|---|
| Entry | $81,834 | $39/hr | $62,208 |
| Mid | $118,600 | $57/hr | $84,649 |
| Senior | $164,854 | $79/hr | $111,962 |
| Lead | $167,226 | $80/hr | $113,362 |
Data Engineer Salary by State (After Tax)
Gross salary, federal tax, state tax, and estimated take-home pay for a Data Engineer in each US state.
Geographic location significantly impacts Data Engineer compensation. The top-paying states for this role include California (tech), Washington (cloud data services), New York (finance data platforms), Virginia (government data), Texas (enterprise data).
States with no income tax (Texas, Florida, Washington, Nevada, Tennessee) offer an effective pay boost of 3-9% compared to high-tax states like California or New York, though these states often compensate with higher cost of living or property taxes. When evaluating offers, consider both gross salary and after-tax take-home pay.
| State | Gross | Federal | State Tax | FICA | Take-Home | Rate |
|---|---|---|---|---|---|---|
| Alabama | $118,600 | $17,711 | $5,765 | $9,073 | $86,051 | 27.4% |
| Alaska | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| Arizona | $118,600 | $17,711 | $2,600 | $9,073 | $89,216 | 24.8% |
| Arkansas | $118,600 | $17,711 | $4,992 | $9,073 | $86,824 | 26.8% |
| California | $118,600 | $17,711 | $7,167 | $9,073 | $84,649 | 28.6% |
| Colorado | $118,600 | $17,711 | $4,558 | $9,073 | $87,258 | 26.4% |
| Connecticut | $118,600 | $17,711 | $5,866 | $9,073 | $85,950 | 27.5% |
| Delaware | $118,600 | $17,711 | $6,597 | $9,073 | $85,220 | 28.1% |
| District of Columbia | $118,600 | $17,711 | $7,240 | $9,073 | $84,576 | 28.7% |
| Florida | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| Georgia | $118,600 | $17,711 | $5,852 | $9,073 | $85,964 | 27.5% |
| Hawaii | $118,600 | $17,711 | $8,857 | $9,073 | $82,960 | 30.1% |
| Idaho | $118,600 | $17,711 | $6,032 | $9,073 | $85,784 | 27.7% |
| Illinois | $118,600 | $17,711 | $5,733 | $9,073 | $86,083 | 27.4% |
| Indiana | $118,600 | $17,711 | $3,617 | $9,073 | $88,199 | 25.6% |
| Iowa | $118,600 | $17,711 | $4,507 | $9,073 | $87,309 | 26.4% |
| Kansas | $118,600 | $17,711 | $6,103 | $9,073 | $85,713 | 27.7% |
| Kentucky | $118,600 | $17,711 | $4,618 | $9,073 | $87,198 | 26.5% |
| Louisiana | $118,600 | $17,711 | $4,459 | $9,073 | $87,357 | 26.3% |
| Maine | $118,600 | $17,711 | $6,942 | $9,073 | $84,874 | 28.4% |
| Maryland | $118,600 | $17,711 | $5,500 | $9,073 | $86,316 | 27.2% |
| Massachusetts | $118,600 | $17,711 | $5,710 | $9,073 | $86,106 | 27.4% |
| Michigan | $118,600 | $17,711 | $4,802 | $9,073 | $87,014 | 26.6% |
| Minnesota | $118,600 | $17,711 | $6,614 | $9,073 | $85,202 | 28.2% |
| Mississippi | $118,600 | $17,711 | $4,996 | $9,073 | $86,820 | 26.8% |
| Missouri | $118,600 | $17,711 | $4,822 | $9,073 | $86,994 | 26.6% |
| Montana | $118,600 | $17,711 | $5,890 | $9,073 | $85,926 | 27.5% |
| Nebraska | $118,600 | $17,711 | $5,406 | $9,073 | $86,410 | 27.1% |
| Nevada | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| New Hampshire | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| New Jersey | $118,600 | $17,711 | $5,429 | $9,073 | $86,388 | 27.2% |
| New Mexico | $118,600 | $17,711 | $4,816 | $9,073 | $87,000 | 26.6% |
| New York | $118,600 | $17,711 | $6,376 | $9,073 | $85,440 | 28.0% |
| North Carolina | $118,600 | $17,711 | $4,763 | $9,073 | $87,053 | 26.6% |
| North Dakota | $118,600 | $17,711 | $2,028 | $9,073 | $89,788 | 24.3% |
| Ohio | $118,600 | $17,711 | $2,681 | $9,073 | $89,135 | 24.8% |
| Oklahoma | $118,600 | $17,711 | $5,143 | $9,073 | $86,673 | 26.9% |
| Oregon | $118,600 | $17,711 | $9,852 | $9,073 | $81,964 | 30.9% |
| Pennsylvania | $118,600 | $17,711 | $3,641 | $9,073 | $88,175 | 25.7% |
| Rhode Island | $118,600 | $17,711 | $4,398 | $9,073 | $87,418 | 26.3% |
| South Carolina | $118,600 | $17,711 | $5,963 | $9,073 | $85,853 | 27.6% |
| South Dakota | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| Tennessee | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| Texas | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| Utah | $118,600 | $17,711 | $5,515 | $9,073 | $86,301 | 27.2% |
| Vermont | $118,600 | $17,711 | $5,902 | $9,073 | $85,914 | 27.6% |
| Virginia | $118,600 | $17,711 | $6,303 | $9,073 | $85,513 | 27.9% |
| Washington | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
| West Virginia | $118,600 | $17,711 | $5,184 | $9,073 | $86,632 | 27.0% |
| Wisconsin | $118,600 | $17,711 | $5,198 | $9,073 | $86,618 | 27.0% |
| Wyoming | $118,600 | $17,711 | $0 | $9,073 | $91,816 | 22.6% |
Top Cities for Data Engineer Pay
San Francisco leads for tech company data platform roles; Seattle strong for AWS/Databricks ecosystem; New York for financial data engineering
When comparing city compensation, factor in cost of living differences. A $118,600 salary in a mid-cost city often provides more purchasing power than a 20-30% premium in San Francisco or New York.
| City | Avg Salary |
|---|---|
| San Francisco, CA | $130,460 |
| Seattle, WA | $130,460 |
| New York, NY | $130,460 |
| Boston, MA | $130,460 |
| Washington, DC | $130,460 |
Calculate Data Engineer Take-Home Pay
Adjust the state and filing status to see your estimated after-tax income.
Estimated Take-Home Pay
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Pay Frequency Breakdown
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How to Become a Data Engineer
Education: The typical path to becoming a Data Engineer involves earning a Bachelor's or Master's in Computer Science, Data Engineering, or Information Systems. Some professionals enter the field through alternative pathways, but formal education provides the strongest foundation for long-term career growth.
Certifications: Key professional credentials for this role include AWS Data Analytics Specialty, Google Professional Data Engineer, Databricks Certified Data Engineer, Snowflake SnowPro Core. These certifications demonstrate expertise to employers and often directly correlate with higher compensation.
Skills & Tools: Proficiency with Python, SQL, Apache Spark, Airflow, Kafka, dbt, Snowflake/Databricks/BigQuery, Terraform, Docker, data pipeline orchestration tools is expected for competitive candidates. Building a portfolio of work or gaining practical experience through internships, projects, or entry-level positions is essential for breaking into the field.
Timeline: Most professionals reach mid-level competency within 3-5 years of entering the field, with senior positions typically requiring 7-12 years of progressive experience.
Data Engineer Career Outlook
Employment for the Data Engineer role is projected to grow 28% from 2022-2032 as organizations build modern data platforms and real-time analytics infrastructure, reflecting strong demand driven by industry evolution and changing workforce needs. The most in-demand specializations include real-time streaming (Kafka/Flink), data lake architecture, data mesh implementation, analytics engineering (dbt), and ML feature engineering.
AI and Automation Impact: AI creates more data to process and manage; data engineers who build AI-ready data infrastructure are commanding top-tier compensation
Professionals who combine deep technical expertise with strong communication skills and adaptability will find the best opportunities in this evolving landscape.
Tax Tips for Data Engineer Earnings
At this income level, you're in the 24% federal bracket and have access to more sophisticated tax reduction strategies:
Backdoor Roth IRA: If your income exceeds direct Roth contribution limits, use the backdoor strategy—contribute to a traditional IRA then convert to Roth. This provides tax-free growth and withdrawals in retirement.
Mega Backdoor Roth: If your employer's 401(k) allows after-tax contributions and in-plan conversions, you can contribute up to $69,000 total (employee + employer) and convert the after-tax portion to Roth—a powerful wealth-building strategy.
SALT Cap Strategy: The $10,000 state and local tax deduction cap may limit your itemized deductions. If you're in a high-tax state, consider strategies like bunching charitable deductions in alternate years using a donor-advised fund.
Tax-Loss Harvesting: If you have taxable investment accounts, systematically harvesting losses to offset gains can save significant taxes while maintaining your investment strategy through substantially different replacement positions.
401(k) + HSA Maximum: Prioritize maxing both accounts—$23,500 (401k) + $4,300 (HSA) = $27,800 in pre-tax deductions, saving you $6,672 in federal taxes at the 24% bracket.
Data Engineer Salary FAQ
The median annual salary for a Data Engineer in the United States is $118,600 in 2026. Compensation typically ranges from $69,800 for entry-level positions to $192,000 for experienced professionals in top-paying markets. Actual pay depends on experience, location, certifications, and employer size.
On a $118,600 salary, a Data Engineer takes home approximately $85,000-$105,000 after federal, state, and FICA taxes, depending on the state and filing status. In no-income-tax states like Texas or Florida, take-home pay is higher than in states like California or New York.
Entry-level Data Engineer professionals with 0-2 years of experience can expect to earn around $81,834 per year. Starting salaries vary significantly by location, with major metro areas offering 15-30% premiums over rural areas.
The highest-paying states for Data Engineer professionals include CA, WA, NY. However, when adjusted for cost of living, some mid-tier states offer better purchasing power. No-income-tax states provide an additional 3-9% effective pay boost.
The median hourly equivalent for a Data Engineer is approximately $57.02, based on 2,080 working hours per year. Actual hourly rates vary by experience level, with senior professionals earning $10-30 more per hour than entry-level.
To become a Data Engineer, you typically need Bachelor's or Master's in Computer Science, Data Engineering, or Information Systems. Valuable certifications include AWS Data Analytics Specialty, Google Professional Data Engineer, Databricks Certified Data Engineer, Snowflake SnowPro Core. Most employers also value practical experience gained through internships or entry-level positions.
Employment for Data Engineer professionals is projected to grow 28% from 2022-2032 as organizations build modern data platforms and real-time analytics infrastructure. AI creates more data to process and manage; data engineers who build AI-ready data infrastructure are commanding top-tier compensation The strongest opportunities are in real-time streaming (Kafka/Flink), data lake architecture, data mesh implementation, analytics engineering (dbt), and ML feature engineering.
A Data Engineer typically spends their day building ETL/ELT data pipelines, designing data warehouse architectures, implementing real-time streaming systems, ensuring data quality and governance, optimizing query performance, managing data lake infrastructure, and creating self-service analytics platforms. The work environment involves data platform teams in tech companies or enterprises; collaborative work with data scientists, analysts, and business stakeholders.