Data Engineer Jobs in USA
If you're looking for high-paying tech jobs in the USA, data engineering offers some of the best opportunities available today. Top positions pay between $160,000 to $180,000 annually. The job market is strong, with over 107,000 data engineer positions open across the country.
Entry-level positions start from $70,000 per year, while mid-career professionals earn between $120,000 and $150,000. Specialized roles like azure data engineering jobs in USA often pay premium salaries due to high demand for cloud skills. Senior data engineers at top companies can earn between $126,100 to $227,950 annually.
In this guide, we'll cover what you need to know about data engineer jobs in USA for 2025, including real salary data from leading companies, required skills, and how to position yourself for these opportunities.
What are Data Engineer jobs in USA?
Data engineers build and maintain the infrastructure that powers analytics and AI systems across organizations. The profession combines software engineering with specialized knowledge of data systems, making it different from other tech roles. As companies handle larger datasets, demand for skilled data engineers continues growing throughout the United States.
Typical Responsibilities and Daily Tasks
Data engineering jobs in USA focus on making data accessible and valuable for business decisions. Data engineers build the pipelines, platforms, and infrastructure that turn raw information into actionable insights. Their role has changed from "back-office builders" to strategic enablers of real-time decision making.
A data engineer's day typically starts with checking automated reports and monitoring data pipelines. They make sure all systems run smoothly and fix any errors quickly.
Their main responsibilities include:
- Designing and implementing ETL (Extract, Transform, Load) processes
- Building and maintaining database pipeline architectures
- Developing algorithms to transform raw data into useful information
- Creating data validation methods and analysis tools
- Ensuring compliance with data governance and security policies
During the workday, data engineers might write Python scripts to extract data from APIs, optimize SQL queries for better performance, or integrate new data sources into existing systems. They work closely with data scientists, business analysts, and management to understand company goals and deliver solutions that meet business needs.
Industries hiring Data Engineers
Several industries lead data engineering recruitment, particularly those handling large volumes of information or undergoing digital transformation.
Healthcare and life sciences rank among top sectors seeking data engineering talent. Hospitals and pharmaceutical companies need data engineers to build secure pipelines that combine patient data, treatment histories, research findings, and real-time sensor information. These systems enable faster diagnoses, personalized medicine, and predictive healthcare approaches.
E-commerce and retail represent another major employer. Companies processing billions of customer interactions, website clicks, purchases, and returns daily require robust infrastructure to capture and process customer behavior at scale. Retail data engineers enable personalized shopping experiences and inventory optimization.
Financial services and fintech organizations need data engineers to build platforms for real-time fraud detection, credit risk assessment, and personalized financial advice delivery. The manufacturing sector shows strong demand as Industry 4.0 brings IoT devices generating constant performance data from machines and production lines.
Media and entertainment companies depend on data engineers to process user engagement data from streaming services, creating recommendation engines and content analytics systems.
Difference between Data Engineers and Data Scientists
Data engineers and data scientists work together but have different focuses, responsibilities, and required skills.
Data engineers build the infrastructure and tools needed for data collection, while data scientists analyze and interpret the prepared data. Data engineers handle the "how" of data processing, while data scientists tackle the "why" and "what if" questions.
In daily work, data engineers deal with raw data that may contain errors, need validation, or include system-specific codes. They build and maintain databases and large-scale processing systems, focusing on data reliability, efficiency, and quality. Data scientists develop models, perform statistical analysis, and create visualizations to extract insights.
The technical skills differ as well. Data engineers typically have stronger backgrounds in programming languages like Python, Java, and SQL, plus expertise in cloud platforms (Azure, AWS, GCP), ETL tools, and big data frameworks like Hadoop and Spark. Data scientists emphasize statistical analysis, machine learning algorithms, and visualization tools.
Both roles remain in high demand across the USA, with companies like Google, Microsoft, Meta, IBM, and Oracle actively seeking qualified data engineers to support their expanding data initiatives.
Top companies hiring for data engineer jobs in USA
The market for data engineer jobs in USA spans multiple sectors, from tech giants to startups and government agencies. Each sector offers different advantages depending on your career goals and preferences.
Google, Amazon, Microsoft
Tech giants remain the biggest employers for data engineers. Google leads with a 4.4 employer rating and headquarters in Mountain View. The company's data engineering team supports infrastructure that powers search algorithms and cloud services. You can expect to work on large-scale distributed systems handling petabytes of data daily.
Amazon maintains strong hiring for data engineers across retail, AWS, and healthcare initiatives. Their Health Store and Tech division seeks data engineers who can "build new engineering solutions" to help customers "access healthcare products and services".
Microsoft, with a 4.0 employee rating, focuses on a growth mindset culture where data engineers collaborate "across boundaries, building upon the ideas of others". With over 60 office locations, you'll find opportunities from Redmond, WA to other tech hubs nationwide.
Startups and mid-size tech firms
Startups offer data engineers greater responsibility and direct business impact. Pogo, a Series A startup with 11-50 employees in Brooklyn, recruits full-stack data engineers for its consumer mobile app helping users "earn and save by unlocking the power of their data". The company reports engagement levels comparable to Instagram and Twitter.
Scale AI, a machine learning platform with 501-1000 employees founded in 2016, provides opportunities for data engineers interested in AI development. Cybersecurity companies like Axonius (201-500 employees) and Dashlane also offer roles in cutting-edge security solutions.
Mid-sized companies provide balanced environments. At Dataonez, data engineers "collaborate with senior engineers and analysts to support data infrastructure needs" while enjoying "competitive salaries, excellent health benefits, and a supportive work culture". These organizations often offer more involvement in business decisions with greater stability than early-stage startups.
Government and healthcare sectors
Government agencies increasingly seek data engineering talent to modernize infrastructure and improve services. Federal positions may require specialized clearances one senior data engineer role at Groundswell requires "Active TS/SCI with CI Poly" clearance.
Local governments also recruit data talent. The City of Oxnard offers hybrid work with salaries between $96,815 and $129,086 for data engineers who can "maintain disparate datasets through ETL pipelines" and "improve data accessibility". These roles typically include retirement plans and tuition reimbursement.
Healthcare represents another growing sector. UnitedHealth Group builds teams for healthcare analytics infrastructure. Amazon's Health Store and Tech division recruits data engineers with "deep technical expertise" to "engineer systems and build reliable and secure services for healthcare".
For entry-level positions, government agencies often provide structured training programs and clearer advancement paths than private sector alternatives. The public sector also offers better work-life balance compared to high-pressure startup environments.
What top companies pay Data Engineers
How much can you expect to earn as a data engineer? The compensation for data engineer jobs in USA shows strong financial rewards across all experience levels. Market data shows that professionals in this field earn impressive salaries that reflect the importance of their work in today's business environment.
What do entry-level data engineers earn?
Entry-level data engineering jobs in USA offer competitive starting salaries. The average entry-level data engineer with less than one year of experience earns approximately $99,000 annually.
Your starting salary will depend on several factors including location, educational background, and specific technical skills. Entry-level data engineers typically earn between $60,000 and $79,000, though this can reach $87,000 in high-demand markets.
Top-paying cities for entry-level positions:
| City | Annual Salary (USD) |
|---|---|
| Burlington, MA | $171,000 |
| San Jose, CA | $163,000 |
| Richmond, VA | $153,000 |
| Boston, MA | $146,000 |
Azure data engineering jobs in USA often pay 10-15% more than comparable generalist positions due to specialized cloud skills.
How much do experienced data engineers make?
Mid-level professionals see significant salary increases. The national average salary reaches approximately $135,000 per year. Total compensation packages often exceed this when you include bonuses and benefits.
Data engineers with 3-5 years of experience typically earn $91,000 to $120,000. Those with 5-7 years of experience can expect $109,000-$144,000. The average total compensation package reaches approximately $153,000 when including additional benefits.
Senior data engineers with 7+ years of experience average $144,000 annually. Top specialists can earn up to $172,000 in base salary, with total compensation reaching $182,000 or more.
Company-specific salary examples:
- Microsoft: $143,000 average annually
- Amazon: $118,000 base salary, $145,000 total compensation
- Google: $126,000 base salary, $160,000 total compensation
What additional benefits can you expect?
Most data engineer jobs in USA include substantial extra compensation beyond base salary. About 80% of early-career data engineers and 88% of senior individual contributors receive bonuses.
Bonus structures typically include individual performance, team achievement, and company-wide results. Early-career professionals average approximately $22,700 in bonuses, while senior-level professionals may receive bonuses averaging $65,400.
Stock options and other perks represent significant value, especially at tech companies and startups. Many organizations offer sign-on bonuses between $5,000 and $10,000. The average additional cash compensation reaches approximately $24,900 annually, with profit-sharing opportunities ranging from $2,000 to $18,300.
Most positions also include health insurance, retirement plans, and flexible work arrangements.
Skills and tools needed for high-paying data engineer jobs
What technical skills do you need for high-paying data engineer positions? You'll need proficiency in programming languages, cloud platforms, and data pipeline frameworks that power modern data infrastructure.
What programming languages do you need?
Python remains the most important programming language for data engineers in 2025. You'll use Python to build efficient data pipelines, automate ETL processes, and perform complex data transformations. The language's libraries like Pandas, NumPy, and SciPy make it ideal for data preprocessing and analysis.
SQL continues to be essential for data engineering work. Despite being decades old, SQL's importance has only grown as organizations handle more data. You'll use SQL daily to model data, extract performance metrics, and develop reusable data structures.
SQL's syntax has evolved to include advanced features that work well with cloud-based data platforms like Google BigQuery, Amazon Redshift, and Snowflake.
Java remains important for building robust, scalable backend systems and data processing applications. Its performance advantages make it valuable for data-intensive applications. Many data engineers work with Java-based frameworks like Hadoop, making it essential for certain roles.
Which cloud platforms should you learn?
Experience with at least one major cloud provider is essential for high-paying data engineer jobs in USA.
AWS leads in ecosystem maturity, offering services like Glue for ETL tasks and Redshift for data warehousing. Many organizations choose AWS due to its complete suite of data engineering tools.
Azure works well for enterprises already using Microsoft products. Azure Synapse Analytics combines big data and data warehousing, while Azure Data Factory handles complex pipeline management. For azure data engineering jobs in USA, skills in Azure HDInsight and Stream Analytics are particularly valuable.
GCP excels at AI-ML integration and real-time analytics. BigQuery offers serverless SQL querying for massive datasets, while Dataflow and Dataproc handle scalable data processing. Companies focused on artificial intelligence often prefer Google Cloud.
What ETL tools and frameworks do you need?
Modern data engineers need proficiency with specialized tools for building data pipelines.
Apache Airflow has become the most widely used platform for orchestrating data workflows. Created at Airbnb, it lets you define pipelines as Python-based DAGs, making it ideal for managing batch ETL jobs.
Data engineers increasingly need to master dbt (data build tool), which helps build reliable pipelines directly within cloud data warehouses. Unlike traditional ETL tools, dbt works within the warehouse for transformations, making it highly efficient.
For real-time data processing, Apache Kafka and Apache Spark are essential tools. Kafka enables real-time data pipelines, while Spark supports both batch and streaming analytics with Python, SQL, Scala, and Java.
How should you approach learning these skills? Focus on developing foundation skills in Python and SQL first, then gradually add cloud platforms and pipeline frameworks. Expanding your skillset to include specialized tools will position you for the highest-paying opportunities.
What should you expect from entry-level data engineering jobs in USA?
Starting your career in data engineering opens opportunities across the United States. Junior positions serve as stepping stones into this field, offering learning experience and competitive compensation from day one.
Common job titles and responsibilities
Junior data engineers build the infrastructure that powers business insights and decisions.
You'll encounter various entry-level job titles including:
- Data Engineer I
- Junior Data Engineer
- ETL Developer
- Associate Data Engineer
- Entry-Level Data Governance Engineer
You'll typically work closely with senior data engineers to design, build, and maintain data pipelines and analytical solutions. Your core responsibilities include developing and maintaining scalable data pipelines, performing data quality checks, troubleshooting infrastructure issues, and helping integrate new technologies into existing systems.
Many entry-level positions involve working with structured and unstructured data sources, including CRM and marketing data platforms. You might also create statistical component demand models or input data into specialized tools for analysis.
Starting salaries for entry-level positions
Entry-level data engineering jobs in USA offer solid starting packages. Junior data engineers typically earn between $70,000 and $100,000 annually. The typical total salary range falls between $87,000 and $137,000 per year according to recent Glassdoor data.
Some companies provide higher compensation, with entry-level roles at firms like Panthalassa offering up to $152,000 annually. Location impacts salary levels significantly, with positions in technology hubs commanding premium compensation.
Many organizations include competitive benefits packages featuring health insurance, 401(k) matching, paid time off, and vision insurance. Some employers offer relocation assistance for promising candidates.
How to stand out as a new graduate
Focus first on mastering fundamental skills. Proficiency in Python and SQL remains essential for any aspiring data engineer. Develop familiarity with ETL processes, databases, and tools like Spark, Snowflake, and BigQuery.
Employers value strong problem-solving skills and attention to detail. Effective communication proves equally important as you'll collaborate across teams.
While many roles traditionally required computer science degrees, the field increasingly welcomes professionals from diverse backgrounds. Creating a portfolio of 2-3 well-documented projects can substitute for work experience by demonstrating your capabilities to recruiters.
For Indian candidates targeting US positions, certifications can provide a competitive edge. Consider pursuing credentials like AWS Certified Data Analytics, Google Professional Data Engineer, or Oracle Cloud Infrastructure certifications to validate your expertise to potential employers.
How to land a data engineer job in USA
Getting data engineer jobs in USA requires more than technical skills alone. You need the right certifications, a strong portfolio, and know where to look for opportunities.
Certifications that boost your chances
Which certifications should you pursue? Industry certifications can dramatically improve your chances of landing data engineer jobs in USA. The AWS Certified Data Engineer - Associate validates 2-3 years of data engineering experience and helps build credibility in data-related roles. This certification stays valid for 3 years, with a 50% discount on your next AWS certification. Professionals with certifications experience lower unemployment levels than those without.
Google Cloud's Professional Data Engineer certification ranked #2 on Skillsoft's list of top-paying IT certifications for 2024. Other valuable certifications include:
- Databricks Data Engineer Associate and Professional ($200)
- Microsoft Azure Data Engineer Associate (DP-203)
- Snowflake SnowPro Core and Advanced Data Engineer
- IBM's Data Engineering Professional Certificate
Building a strong portfolio and resume
How can you showcase your skills effectively? Create 2-3 well-documented projects that demonstrate ETL pipelines, cloud data warehousing skills, and real-time processing capabilities. Document your process thoroughly in READMEs, explaining project goals, architecture decisions, data sources, and lessons learned.
For your resume, include quantifiable achievements rather than just listing responsibilities. Highlight end-to-end project involvement and the business impact of your solutions. Customize your resume for each application using keywords from the job posting.
Where to find the best job listings
LinkedIn serves as the leading platform for data engineer jobs in USA, offering valuable "Easy Apply" features and insights into your connections at target companies. Glassdoor provides job listings alongside salary information and interview questions.
For remote opportunities, explore specialized platforms like Remote OK, Working Nomads, and We Work Remotely, which attract over 100,000 monthly users. Career Vault aggregates listings from various sources, providing access to opportunities across industries.
Frequently Asked Questions
What is the average salary range for data engineers in the USA?
Data engineer salaries vary by experience. Entry-level roles typically start around $70,000–$100,000 annually, mid-level professionals earn $120,000–$150,000, and senior data engineers at top companies may command $160,000–$180,000 or more.
How in-demand are data engineering jobs?
Data engineering roles remain highly in demand, with well over 100,000 openings across the USA as businesses scale analytics, AI, and real-time data systems.
What skills are essential for high-paying data engineer positions?
Core skills include Python, SQL, and Java; cloud expertise (AWS, Azure, GCP); and ETL/pipeline tools such as Apache Airflow and dbt. Familiarity with Spark/Kafka is a plus.
Which industries are hiring the most data engineers?
Healthcare & life sciences, e-commerce/retail, financial services, manufacturing, and media & entertainment are leading employers, relying on robust data infrastructure.
How can I improve my chances of landing a data engineer job in the USA?
Earn relevant certifications (e.g., AWS Certified Data Engineer, Google Cloud Professional Data Engineer), build a portfolio of 2–3 projects, tailor your resume to measurable outcomes, and use platforms like LinkedIn and Glassdoor (plus remote job boards) for broader reach.