How Much Should You Pay a Data Engineer? US vs. Offshore Costs

How Much Does It Cost to Hire Data Engineers in the US?

Hiring a full-time data engineer in the US doesn’t just mean covering a six-figure salary. You’re also looking at employer taxes, healthcare benefits, equipment, PTO, and office overhead if they’re on-site. 

These extras can raise the total cost of employment by 20 to 30 percent.

Based on what we see, here’s what companies are paying:

• Entry-level data engineer (0–2 years): $85,000 to $158,000
• Mid-level data engineer (2–5 years): $109,000 to $200,000
• Senior data engineer (5+ years): $208,000 to $306,000
• Data engineering manager: Often well above $200,000

In states like California or New York, salaries are on the higher end of the averages due to the high cost of living.

According to Levels.fyi, senior data engineer salaries in San Francisco can hit $402,000. Even in cities like Dallas or Atlanta, six-figure compensation is standard.

These salaries are fueled by a sharp rise in demand. Data engineering is one of the fastest-growing tech roles, with AI driving much of the momentum. However, only about one-quarter of job postings actually include salary information, making it tough for employers to benchmark.

Offshore Hiring: How Data Engineer Salaries Compare by Region

For many US businesses, hiring local data engineers just isn’t sustainable. Salaries alone are high, but once you layer in employer taxes and benefits, you’re looking at a serious dent in your budget. That’s why more companies are exploring offshore hiring: not just to cut costs but to access a larger talent pool to find the exact expertise they need.

And offshore hiring doesn’t have to mean middle-of-the-night standups. Nearshoring, or hiring from nearby countries like those in Latin America, offers a balance of affordability and real-time collaboration.

Why LatAm is a smart nearshore option

Latin America is especially attractive for US employers. The region shares the same or overlapping working hours, cultural alignment, and a strong educational base in STEM. Here’s what data engineer salary ranges look like in LatAm, based on our experience sourcing for these roles:

Hiring in LatAm can easily save you 50–70% compared to US salaries while maintaining strong performance and day-to-day communication. If you’re looking to find a remote data engineer, this region is worth prioritizing.

Offshore markets: What about other regions?

While Latin America is our specialty, we also looked at global salary data from public sources. These figures aren’t as reliable as our LatAm benchmarks, but they offer a rough sense of market rates:

• India: ~$35,700/year – Lower average salary range, but big time zone differences. (Levels.fyi)
• Philippines: ~$22,500/year – Affordable, but also far from US time zones. (Levels.fyi)
• Poland: ~$84,000/year – High-quality talent, closer to US working hours than Asia. (Levels.fyi)
• UK: ~$122,700/year – Comparable to US costs in many cases. (Levels.fyi)
• Singapore: ~$137,700/year – Premium talent, premium price. (Levels.fyi)
• South Africa: ~$34,800/year – English-speaking, more overlap than Asia (Levels.fyi)

What Drives Data Engineering Salaries Up or Down?

Why does a data engineer’s salary vary so much from one company or country to another? The short answer: Factors like cost of living, company size, required specialization, and more all play a role.

Here are the biggest factors that influence pay when hiring a data engineer:

• Location and cost of living: A junior data engineer in San Francisco will cost far more than one in Bogotá, even if their skill sets are comparable.
• Industry demand: Companies in AI, finance, and healthcare tend to offer higher salaries to stay competitive.
• Experience level: An entry-level data engineer might handle documentation or support work, while a senior or data engineering manager is likely leading system architecture and strategy.
• Specialized skills: Roles like cloud data engineer, big data engineer, or data warehouse engineer often come with higher pay due to their niche focus.
• Company size and hiring budget: Larger enterprises may offer more compensation and benefits than early-stage startups.
• Remote vs. on-site: On-site roles in expensive cities tend to offer higher base salaries.
• Services needed: Salaries can shift depending on whether the engineer is building pipelines, working in data ops, or focused on analytics.

Data engineers have a wide range of skills, and some skill sets are more valuable than others. Data engineers skilled in emerging areas like AI, machine learning, and real-time data processing can command a higher salary than traditional roles.

How to Set the Right Salary When Hiring Data Engineers

Building a smart salary offer starts well before negotiations begin. In a market where demand often outpaces supply, it’s important to have a strategy that balances compensation with flexibility and long-term value.

Here’s how to build a competitive offer:

1. Start with market research: While this article gives you baseline salary ranges, you’ll need to dig deeper into the specific region you’re targeting. Salary expectations can vary significantly within regions or even between cities within the same country.
   For example, if you’re hiring in Mexico City versus Guadalajara or Buenos Aires versus smaller Argentinian cities, local economic conditions will impact competitive rates.
   Use specialized salary tools for your chosen location and connect with local recruiters who understand regional nuances that national averages might miss.
2. Go beyond the base salary: If you can’t match US compensation, consider what else you can offer. Many candidates value benefits and perks like flexible hours, wellness stipends, equipment budgets, or remote-first policies.
3. Stay open to negotiation: Leave room in your initial range to adjust based on candidate expectations and experience.
4. Review and adjust over time: Annual raises, project-based bonuses, or promotions can help retain talent and show long-term investment.
5. Partner with a recruitment agency: Especially if you’re hiring offshore, a local recruiting partner can help you understand what competitive offers look like and what benefits are considered standard in that region.

Some employers also explore working with freelance data engineers, who can be cost-effective. However, going down this route may come with additional considerations like limited availability.

Remember that there’s more to a great offer than numbers alone. You also need to show candidates that you value what they bring to your team. If you’re not sure where to start, Near’s guide to making a good job offer breaks it down.

Final Thoughts

Data engineer salaries can vary a lot depending on location, experience, and skill set. For many US companies, especially startups or smaller teams, hiring locally can be hard to justify financially.

Offshore hiring helps stretch those budgets without sacrificing quality. Latin America is a great option for hiring a data engineer because it offers strong talent, lower rates, and real-time collaboration, thanks to the same or close time zones with US locations.

If you’re looking for more specific salary data points or wondering exactly how much you might save in countries like Argentina, Mexico, or Colombia, our team runs free consultations. Book a consultation call, and we’ll share insights on data engineering roles across different LatAm markets based on what we’re seeing in real hiring situations right now.

And if you want a deeper dive into the benefits of building a nearshore data team beyond just the numbers, our guide on why and how to hire nearshore data engineers covers everything. It’s a straightforward resource for companies considering this route for the first time.