Data engineers keep modern businesses running by building pipelines, managing data infrastructure, and making information usable across analytics, AI, and software teams. That technical depth also makes data engineer salaries an important consideration for companies building data teams in 2026.
Compensation varies widely by experience, location, cloud expertise, and technical stack. A senior engineer working with Snowflake, Databricks, AWS, or Apache Spark will typically command far more than someone in an entry-level role. For employers, knowing those benchmarks makes it easier to set a competitive hiring budget.
In this guide, we'll compare average data engineer salaries in the U.S. and Latin America, break down pay by seniority and country, and examine the skills that increase compensation. If you're focused on the hiring process itself, our guide to hiring data engineers covers that in more detail.
Data Engineer Salary in 2026: Key Numbers
Data engineer salaries show a wide gap between the U.S. and Latin America. As of August 2026, ZipRecruiter places the average U.S. data engineer salary at about $129,700 per year, or roughly $10,800 per month.
For comparison, South's current salary benchmarks put the average salary for a Latin American data engineer at approximately $3,500 per month, or $42,000 per year. That creates potential salary savings of about 68% for a U.S. company hiring comparable talent in the region.
These are useful starting points for a 2026 hiring budget, but the actual data engineer salary can move considerably based on seniority, country, technical skills, and specialization. Experience with platforms such as Snowflake, Databricks, and AWS, as well as modern data pipelines, can boost compensation, especially for senior data engineers.
For companies evaluating LATAM talent, South's Data Engineer hiring page provides additional benchmarks for hiring data engineers across Latin America.
Average Data Engineer Salary in the U.S.
The average data engineer salary in the U.S. is around 123,000–130,000 per year in 2026, although estimates vary by source. ZipRecruiter reports an average of $129,716 per year, while Salary.com puts the figure at roughly $123,053.
Seniority makes a significant difference. Salary.com estimates compensation ranging from about $77,600 for a Data Engineer I to nearly $158,000 for a Data Engineer V.
For employers, job title alone isn't enough to set a competitive offer. A senior data engineer who can design cloud architecture, optimize complex data pipelines, and work independently across teams can command considerably more than someone with the same title but a narrower scope.
Companies should also consider technical specialization when building a data engineer hiring budget. Experience with AWS, Azure, Google Cloud, Snowflake, Databricks, Spark, Kafka, Airflow, and dbt can influence compensation, particularly for roles involving large-scale or real-time data infrastructure.
If you're still defining the level your team needs, South's guide to hiring data engineers covers responsibilities, skills, and candidate profiles in more detail.
Average Data Engineer Salary in Latin America
A data engineer salary in Latin America can vary considerably by seniority, technical specialization, and experience working with international teams. As a broad benchmark, South places the average LATAM data engineer salary at around $3,500 per month, although experienced engineers can earn considerably more.
For more specialized profiles, South's current data engineering benchmarks put mid-level professionals at roughly 45,000–62,000 per year and senior data engineers at 68,000–88,000. Data architects and highly experienced specialists can earn up to 85,000–110,000 annually.
The strongest LATAM candidates won't necessarily sit near the regional average. Engineers with deep experience in Snowflake, Databricks, Spark, Kafka, AWS, Azure, or complex data architecture can command a premium, particularly when they have strong English skills and experience collaborating with U.S. companies.
Even at the higher end of these ranges, Latin American compensation can remain well below equivalent U.S. salaries, which is why the region has become an attractive market for companies building experienced remote data teams.
U.S. vs. Latin America Data Engineer Salaries by Seniority
The salary gap between the U.S. and Latin America remains significant across experienced data engineering roles. The biggest opportunity isn't simply finding the lowest salary. It's getting the level of experience you need at a more manageable cost.
Using the salary benchmarks above, Latin American data engineers can cost roughly 27% to 50% less in base salary than comparable U.S. roles, depending on seniority.
For companies building a mature data function, mid-level and senior engineers often provide the strongest balance of experience and cost savings. These professionals may already have several years of experience with data pipelines, cloud platforms, warehouses, and production systems, yet still earn a lower salary than equivalent U.S. hires.
The exact difference will depend on the candidate's country, English proficiency, technical stack, and experience working with international companies. Highly specialized LATAM data engineers can command salaries near the top of the regional range, especially for skills such as Snowflake, Databricks, Spark, Kafka, and cloud data architecture.
Data Engineer Salaries by Latin American Country
Latin America isn't a single salary market. Data engineer compensation can vary significantly from one country to another, depending on talent supply, local demand, seniority, and candidates' experience working remotely with U.S. companies.
For remote data engineers hired by international employers, 2026 market benchmarks show annual salaries ranging from roughly $30,000 at the lower end to $88,000 for experienced professionals in higher-paying LATAM markets.
These figures are best treated as hiring benchmarks rather than fixed salary bands. They cover a broad range of experience levels, so a mid-level data engineer will usually fall somewhere in the middle while senior specialists can approach or exceed the upper end.
Chile and Uruguay tend to sit at the higher end of these remote salary benchmarks, while Colombia, Peru, Mexico, Argentina, and Brazil offer a wider range of compensation options.
Country matters, but the candidate's capabilities usually matter more. A data engineer with strong experience in Python, SQL, Snowflake, Databricks, Spark, or cloud architecture may command substantially more than the local average, especially when they have strong English skills and experience supporting U.S. teams.
Companies exploring individual markets can also see our guides to hiring developers in Chile and outsourcing software development to Argentina for more context on the regional talent landscape.
How Data Engineer Salaries Change by Specialization
Two data engineers with the same number of years of experience can have very different compensation. Specialization matters because some data environments require deeper technical knowledge, larger-scale systems, or more responsibility for architecture and reliability.
Cloud Data Engineer
Cloud data engineers work with platforms such as AWS, Azure, and Google Cloud to build and maintain scalable data infrastructure. Experience with cloud data warehouses such as Snowflake, BigQuery, and Redshift can strengthen a candidate's earning potential.
Big Data Engineer
Big data engineers specialize in processing very large datasets using technologies such as Apache Spark and distributed computing frameworks. These roles often command higher salaries when engineers are responsible for performance, scalability, and complex data pipelines.
Streaming Data Engineer
Engineers with experience in Apache Kafka, Flink, and real-time data processing can also command a premium. They're commonly needed when businesses depend on continuously updated information for analytics, transactions, monitoring, or customer-facing products.
Data Platform Engineer
A data platform engineer typically works across infrastructure, orchestration, observability, governance, and developer tooling. The broader scope of the role can push compensation above that of a more narrowly focused data engineer.
AI and Machine Learning Data Engineer
As companies build more AI products, demand is growing for engineers who can prepare training data, build ML pipelines, manage vector databases, and support production AI systems. Experience connecting traditional data engineering with machine learning infrastructure can make these professionals especially valuable.
Employers should budget for the work a data engineer will actually own, rather than relying on the job title alone. A role involving architecture, real-time systems, cloud infrastructure, or AI pipelines will typically require a stronger and more expensive candidate profile.
Which Skills Increase a Data Engineer’s Salary?
A data engineer’s salary often rises with the complexity of the systems they can build and manage. Employers tend to pay more for professionals who can work independently across the full data lifecycle, from ingestion and transformation to cloud architecture and performance optimization.
Some of the most valuable data engineering skills in 2026 include:
- SQL and Python: Core skills for building pipelines, transforming datasets, and automating data workflows.
- Snowflake and Databricks: Experience with modern cloud data platforms can increase a candidate's demand. See our guide to Snowflake talent.
- AWS, Azure, and Google Cloud: Cloud expertise is especially valuable for engineers responsible for scalable data infrastructure.
- Apache Spark and Kafka: These skills are often associated with large-scale processing and real-time data engineering.
- dbt and Airflow: Common tools for data transformation and workflow orchestration.
- ETL and ELT pipelines: Companies value engineers who can design reliable processes to move and transform data.
- Data modeling and architecture: Senior professionals who can make architectural decisions typically command higher compensation.
The highest salaries usually go to engineers who combine several of these skills rather than specializing in a single tool. For example, someone who can design a Snowflake architecture, build Python pipelines, orchestrate workflows with Airflow, and deploy infrastructure on AWS brings more value than a candidate whose experience is limited to one part of the data stack.
This is also why technical scope should be considered alongside years of experience when setting a competitive data engineer salary.
What Factors Affect Data Engineer Salaries?
Data engineer compensation depends on more than years of experience. The scope of the role, the technical environment, and the level of ownership can all affect what a company should expect to pay.
Experience and Seniority
Junior data engineers usually focus on building and maintaining pipelines, while senior professionals may own architecture, mentor teammates, and make decisions that affect the entire data platform. That added responsibility typically leads to higher compensation.
Technical Stack
Experience with in-demand tools such as Snowflake, Databricks, Spark, Kafka, Airflow, and dbt can increase a candidate’s market value, especially when those technologies are central to the company’s infrastructure.
Cloud Expertise
Strong AWS, Azure, or Google Cloud experience can boost salaries, as many modern data engineering roles involve designing, deploying, and optimizing cloud-based systems.
Industry
Compensation can also vary by sector. Companies in fintech, SaaS, AI, healthcare technology, and other data-intensive industries may pay more for engineers who understand complex, high-volume, or regulated environments.
Company Size and Role Scope
A data engineer maintaining established pipelines may earn less than someone responsible for designing a new data platform from scratch. Larger teams may also hire more specialized profiles, while smaller companies often look for engineers who can cover a broader range of responsibilities.
English and International Experience in Latin America
For LATAM data engineers working with U.S. companies, strong English communication and previous experience on international teams can support higher salary expectations. These skills make it easier to collaborate directly with product, analytics, engineering, and leadership teams across time zones.
Leadership and Architecture Responsibilities
Senior engineers who influence data strategy, design architecture, review technical decisions, or lead other engineers generally command the highest salaries. At that level, companies are paying for both technical execution and decision-making ability.
Data Engineer Salary vs. Other Data Roles
Data engineering sits toward the higher end of the data salary spectrum, but compensation overlaps with several adjacent positions. The right comparison depends on what you actually need the person to build, analyze, or maintain.
South's current benchmarks place a data analyst at about $6,900 per month in the U.S. versus $2,500 per month in Latin America, while a data scientist averages roughly $10,200 per month versus $3,250 per month.
More engineering-heavy positions tend to command higher salaries. South benchmarks analytics engineers at around $11,000 per month in the U.S. and $5,150 per month in Latin America, while machine learning engineers average about $11,000 and $5,500 per month, respectively.
A higher salary doesn't automatically mean a role is the better hire. Companies that need reliable pipelines and data infrastructure should prioritize a data engineer. Teams focused primarily on dashboards may need a data analyst, while predictive modeling and production ML call for more specialized data science or machine learning talent.
For a deeper look at where responsibilities overlap, see our Data Engineer vs. Data Scientist comparison.
How Much Can Companies Save by Hiring Data Engineers in Latin America?
For U.S. companies, the salary difference between domestic and Latin American data engineers can translate into tens of thousands of dollars in annual savings per hire.
Using the salary benchmarks above, a company hiring a mid-level data engineer in Latin America could save roughly 27,000–44,000 per year compared with a similar U.S. hire. At the senior level, the difference can reach 33,000–53,000 annually.
For a company building an entire data team, those differences can compound quickly. Hiring three senior data engineers in Latin America at typical U.S. salary levels could free up more than $100,000 in the annual hiring budget.
The goal, though, is to compare candidates with similar levels of experience and technical capability. Senior LATAM data engineers with strong English, U.S. work experience, and expertise in tools such as Snowflake, Databricks, AWS, Spark, and Kafka will usually command salaries toward the higher end of the regional range.
That still gives companies room to build experienced data teams while keeping compensation more manageable.
When Does Hiring a Data Engineer in Latin America Make Sense?
Hiring a data engineer in Latin America can make sense when you need experienced technical talent but want more flexibility in your engineering budget. The region is especially attractive for companies building long-term remote data teams that need close collaboration with U.S. colleagues.
It can be a strong fit when you’re looking for:
- Mid-level or senior data engineering experience
- Strong SQL, Python, Snowflake, Databricks, or cloud skills
- Significant U.S. time-zone overlap
- English-speaking professionals who can collaborate across teams
- Full-time remote talent rather than short-term project support
- Lower salary benchmarks than comparable U.S. hires
For teams weighing different global hiring models, our nearshore vs. offshore outsourcing guide explains how Latin America compares with more distant talent markets.
The best results usually come from treating LATAM hiring as a talent strategy rather than a cost-cutting exercise. Competitive local compensation, clear career growth, and strong integration with the U.S. team can help companies attract and retain better data engineers for longer.
Hire Data Engineers in Latin America With South
Finding a strong data engineer takes more than matching a résumé to a list of tools. You need someone who can work with your existing stack, communicate clearly with the rest of the team, and handle the level of ownership the role requires.
South helps U.S. companies find pre-vetted data engineers across Latin America for full-time remote roles. Candidates can bring experience with SQL, Python, Snowflake, Databricks, AWS, Azure, Spark, Airflow, dbt, and other modern data technologies.
You also get access to salary benchmarking, English-speaking talent, strong U.S. time zone overlap, and a single consolidated monthly invoice. The goal is to help you find the right technical fit while making your hiring budget go further.
If you're planning to expand your data team, schedule a free call and find remote talent in Latin America with South.
Frequently Asked Questions (FAQs)
What is the average data engineer salary in the U.S. in 2026?
The average data engineer salary in the U.S. is around $123,000 to $130,000 per year in 2026, depending on the source and role definition. Senior and highly specialized data engineers can earn significantly more.
How much does a data engineer make in Latin America?
A data engineer in Latin America typically earns around $42,000 per year, although compensation varies widely by seniority, country, and technical specialization. Experienced engineers working with international companies can earn $70,000 or more.
How much does a senior data engineer make?
In the U.S., a senior data engineer can earn around $120,000 or more per year. In Latin America, senior data engineer salaries commonly fall in the $68,000 to $88,000 range for professionals working with international employers.
Which Latin American countries have the highest data engineer salaries?
Markets such as Chile and Uruguay often sit toward the higher end of regional salary benchmarks, while Argentina, Brazil, Colombia, Mexico, Peru, and Costa Rica also have strong data engineering talent pools. The candidate’s experience and technical skills usually matter more than country alone.
Are data engineers paid more than data scientists?
Data engineer and data scientist salaries are often fairly close. Data engineers may earn more when the role requires advanced cloud architecture, distributed systems, real-time pipelines, or large-scale infrastructure. You can see a broader role comparison in our Data Engineer vs. Data Scientist guide.
What data engineering skills command the highest salaries?
Skills such as Python, SQL, Snowflake, Databricks, Apache Spark, Kafka, AWS, Azure, Google Cloud, Airflow, dbt, and data architecture can all increase earning potential. Engineers who combine several of these skills and can own complex systems tend to command the highest salaries.
Is data engineering still a high-paying career in 2026?
Yes. Data engineering remains one of the better-paid technical career paths because companies still need professionals who can build reliable data pipelines, manage cloud infrastructure, and support analytics and AI initiatives.
How much can a U.S. company save by hiring a data engineer in Latin America?
Depending on seniority, a U.S. company may save roughly 27% to 50% in base salary by hiring a comparable data engineer in Latin America. Senior and staff-level hires can translate into annual savings of tens of thousands of dollars per role.
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