Top Data Engineering Service Providers in 2025

Why Businesses Outsource Data Engineering Services

Hiring in-house isn’t always the most practical path. This is especially true now that the tech talent pool is tight and salaries are climbing. For many businesses, outsourcing data engineering offers a faster, more flexible way to scale.

Cost is one of the biggest drivers. Full-time data engineers in the US can come with six-figure salaries, plus benefits, overhead, and onboarding time. 

And even with a substantial budget, it’s still tough to fill skill gaps in your data team, especially when it comes to specialized areas like real-time data streaming and cloud migration.

Outsourcing lets companies bypass local hiring challenges and access niche technology expertise on demand. Whether you need help building a data pipeline or architecting a cloud warehouse, you can bring in the skills you need when you need them at a cost savings.

It also offers project-based flexibility. Instead of hiring full-time staff for short-term needs, companies can work with partners who deliver results without long-term commitments.

If you're trying to balance quality and cost, outsourcing is a smart place to start. This becomes readily apparent once you understand the average salaries across data roles.

7 Essential Data Engineering Services Your Business Might Need

When you start looking into data engineering solutions, it’s easy to get overwhelmed by all the buzzwords. But the truth is that businesses rely on a few core services to manage, move, and make sense of their data. 

Here’s a breakdown of the most common ones:

1. Data pipeline development: The backbone of any modern data system, pipelines move data cleanly and reliably from one system to another. Many teams now apply DevOps principles to automate and monitor these pipelines, making them faster to deploy and easier to scale. Good pipelines are essential for real-time insights and efficient reporting.
2. ETL processes: ETL helps you pull raw data from multiple sources, clean it up, and load it into storage systems where it can be used. This data engineering service is key to making data usable for analysis.
3. Data warehousing and architecture: A well-designed warehouse—where data from various sources are integrated and systematically stored—helps teams access structured data efficiently for streamlined reporting and analysis. It provides critical infrastructure for everything from business intelligence dashboards to complex predictive models, making it an essential component for companies scaling their operations and data capabilities.
4. Real-time data processing: Whether you’re running fraud detection or live dashboards, this service enables instant insights.
5. Cloud data integration (AWS, Azure, GCP): Many businesses now depend on cloud-based infrastructure support, making this one of the most in-demand services.
6. Data governance and quality: Making sure that your data is accurate, consistent, and compliant is critical, especially in regulated industries.
7. Data lake implementation: For businesses handling unstructured or large-scale data, a data lake is a cost-effective way to store and manage it. It’s a must-have for many big data architecture support strategies.

Whether you're building your data infrastructure from scratch or scaling existing systems, these data engineering services provide the essential foundation needed for smart, data-driven decision-making. They equip your organization with the technical capabilities to collect, process, and analyze data efficiently, transforming raw information into actionable business intelligence.

Should You Outsource to a Data Engineering Company or Hire an Individual Engineer?

If you’re exploring outsourcing, chances are you’re already aware of the prohibitive costs of building a data team locally. Before jumping into partnerships, though, one of the first decisions to make is whether you want to work with an individual engineer or a full-service company.

Hiring a freelance or contract-based data engineer can be a smart move, especially for startups or teams with technical leadership already in place. When you hire remote data talent, you have more control over who joins your project and how they work. These engineers often integrate directly into your existing team, creating more of a team extension than a complete handoff.

In contrast, a data engineering company offers managed solutions with full teams, including project managers, QA specialists, and cloud experts. This approach works well if you need to outsource data engineering effectively across multiple work streams or prefer minimal direct oversight of the engineering process.

Cost is another factor. Freelancers and individual contractors can be more affordable, especially when businesses tap into nearshore hiring options, where top talent is often available at a significant cost savings compared to US rates. However, agencies bring delivery guarantees, broader skills coverage, and typically faster ramp-up times for large-scale projects.

If you’re unsure which path fits your needs, start by looking at your project’s size, timeline, and internal resources capacity. And make sure you understand the type of engineer you may need so you can decide who’s best suited to do the work.

5 Top Data Engineering Service Providers to Know in 2025

Your choice of data engineering partner can directly influence how much value you get from your data. We’ve put together the following unranked list to help you explore some options and make an informed decision with confidence.

While these five companies all deliver data engineering excellence, their approaches, specializations, and pricing models vary, giving you options—whether you need to build a team or fully outsource your next data project. Some are recruitment and staffing agencies that can help you find the specialized talent you need, while others are consulting firms or data engineering services providers.

1. Near

Up first is Near. That’s because we understand what great data engineers bring to the table. We also know that US businesses deserve access to high-quality data talent without the enterprise price tag. 

We specialize in sourcing pre-vetted talent from Latin America, including data engineers, who can deliver the same level of technical expertise as local hires at up to 50% lower cost. 

Whether you're scaling fast or filling a critical role, we handle the recruiting so you can focus on what matters.

Key features:

• Targeted recruiting: Our team sources talent with deep expertise in modern data engineering technologies like ELT processes, pipeline orchestration, and cloud data stacks. We identify and vet engineers who have proven experience implementing these solutions successfully.
• Cultural and time zone alignment: Latin American talent works US hours and understands US business practices.
• Flexible engagement models: Customize the services you receive from us, choosing between recruitment and staffing models.

2. Exomindset

Exomindset sources data engineering talent and delivers platform-focused consulting from its base in Argentina, with additional development hubs in Brazil, Spain, and Albania. It also operates a US office in Florida. 

The company works with clients to implement systems for ETL/ELT, data lake and lakehouse structures, and custom pipeline orchestration. Exomindset supports nearshore hiring for businesses that need contract-based engineers or external teams to manage core data operations.

Key features:

• Talent sourcing: Offers access to nearshore engineers located across Latin America and Europe.
• Delivery model: Uses agile methodology with sprint planning, iterative delivery, and regular feedback cycles.
• Service scope: Covers governance strategy, data architecture consulting, and DataOps implementation for mid- to large-scale systems.

3. DS Stream

DS Stream is a Poland-based company offering full-service data analytics engineering and architecture consulting, with a US office located in Washington. It focuses on building scalable systems for real-time and batch data processing, database optimization, and ETL pipeline development. 

DS Stream works with large volumes of structured and unstructured data and supports cloud-based deployments across AWS, Azure, and GCP. Its team includes certified engineers and consultants specializing in analytics-driven infrastructure and workflow optimization.

Key features:

• Engagement focus: Works with enterprise clients on long-term architecture and transformation projects.
• Technology stack: Uses BigQuery, Redshift, Databricks, Airflow, Spark, Hadoop, and multiple NoSQL databases.
• Service range: Offers maturity assessments, database tuning, and integration of custom data pipelines across hybrid cloud environments.

4. LatentView

LatentView is a global data services firm with offices in the US, India, Canada, the UK, the EU, and Singapore. Its data engineering team focuses on data platform modernization, governance frameworks, and automated data migration across cloud and hybrid environments. 

LatentView also offers integration services for cross-system data access and self-service analytics. Its proprietary tool, MigrateMate, supports platform-agnostic migration with built-in encryption and validation protocols.

Key features:

• Team structure: Employs cross-functional teams of data engineers, architects, and governance consultants.
• Industry focus: Works with clients in finance, retail, CPG, and industrial operations.
• Compliance support: Implements controls aligned with global data security and privacy regulations.

5. LeewayHertz

LeewayHertz is a US-based data engineering-as-a-service (EaaS) company with offices across Latin America, India, Africa, Australia, Asia, and Europe. It specializes in AI-driven data engineering and machine learning (ML) pipeline development. Its offerings include data warehousing, compliance-focused governance, and infrastructure for scalable data processing. 

Its services support data labeling, model deployment, and ML application integration, with tooling aligned to leading AI models such as GPT-4o, Llama3, and Claude.

Key features:

• AI/ML specialization: Works with advanced neural networks and custom model development for NLP, CV, and predictive analytics.
• Engagement models: Offers team extension, dedicated teams, and project-based delivery across regions.
• Tooling stack: Uses TensorFlow, PyTorch, Kubernetes, Docker, PostgreSQL, and multiple cloud platforms (AWS, Azure, GCP).

Final Thoughts

There’s no single way to build a great data team. Some businesses are looking for full-service support to take projects off their plate completely. Others want hands-on control and prefer hiring individual engineers who can plug into their existing workflows.

The best setup depends on what you're building, how quickly you need to deploy, and what internal resources you already have. Whether you're looking for a short-term specialist or a long-term partner, the provider you choose will shape how useful and accessible your data becomes.

At Near, we can help your company grow smart by connecting you with experienced data engineers in Latin America. Our candidates work the same hours, speak your language, and bring the kind of technical background that fits right into your stack.

Book a consultation, and let’s talk about your data needs. We’ll help you figure out what makes the most sense for your team and your goals.