Hiring data engineers isn't just about filling a technical role. It's about finding the right people to support your entire data strategy. Good data engineers design and maintain systems to handle your information so that everyone on your team has clean, reliable, and well-organized data to refer to. But hiring can be tough.
Posting generic job descriptions, conducting drawn-out technical interviews, and hoping for the best simply doesn't work in today's competitive market. Most companies spend 2-4 months trying to hire data engineer talent. During this period, many qualified candidates get hired faster by competitors, creating a stall in your project pipelines.
While you're navigating lengthy hiring processes, your competitors are building robust data infrastructure with trained, vetted remote data engineers who integrate seamlessly into existing tech stacks within weeks. The companies winning the data race aren't necessarily smarter. They just know how to access qualified talent faster and more efficiently.
So how can you hire the best data engineering talent in 2025? This guide will point you in the right direction.
What Does a Data Engineer Actually Do?
Before hiring data engineers, it's important to understand what this job role actually entails.
In a nutshell, data engineers build and maintain the foundational infrastructure for your data. They make sure your data is accessible, reliable, and actionable across your organization. On a day-to-day basis, data engineers perform job functions such as:
- Designing and implementing comprehensive data pipelines, using orchestration tools like Apache Airflow
- Managing distributed processing with Apache Spark
- Optimizing cloud-native architectures on platforms including Snowflake, AWS, Azure, and Google Cloud
- Working with data transformation tools like dbt
- Implementing real-time streaming with Apache Kafka
- Ensuring data quality through automated testing and monitoring frameworks
Beyond technical implementation, experienced data engineers collaborate with data scientists, analysts, and business stakeholders to align infrastructure capabilities with organizational requirements.
They understand data governance principles, implement comprehensive security protocols, and design systems that optimize for performance, cost, and scalability.
Why Hiring Data Engineers is Difficult
Hiring data engineers can be a challenge because of increased demand and evolving technical requirements. Although data engineering roles are experiencing 50% year-over-year job growth, hiring one of these professionals isn't always easy.
This immense growth stems largely from AI companies and organizations racing to modernize legacy data infrastructure. Every business now recognizes that its competitive advantage depends on how quickly it can collect, process, and act on data insights.
Data engineer salaries reflect this intense demand, averaging at $129,716 annually, with mid-level data engineers earning between $117,873 and $150,488. Senior-level professionals command $140,311 to $174,892.
Once the go-to region for budget-friendly tech talent, Eastern Europe is no longer the bargain it used to be. The IT outsourcing market surged from $9.93 billion in 2020 to $14.39 billion in 2024, a compound annual growth rate of around 11–12%, reflecting escalating value and likely escalating salaries as well.
The scarcity of qualified candidates has created a market where top data engineers receive multiple offers and make decisions quickly. Organizations with long-drawn processes lose preferred candidates to competitors who can move from initial contact to offer within days rather than months.
How to Hire a Data Engineer
Successfully hiring data engineer talent requires abandoning traditional software development recruitment approaches in favor of specialized strategies designed for infrastructure roles:
Define Infrastructure Objectives Clearly
Begin with specific technical outcomes rather than generic role descriptions.
Are you implementing real-time streaming capabilities for customer analytics? Modernizing legacy ETL processes that currently take hours to complete? Building a comprehensive data lakehouse architecture to support machine learning initiatives?
A clear, objective definition enables more effective candidate evaluation.
Choose the Appropriate Experience Level
As with many professional roles, data engineers vary by experience and knowledge level:
- Junior data engineers work well for established processes with strong mentorship structures but require significant guidance and development time.
- Mid-level professionals can architect and implement complex solutions independently while maintaining cost-effectiveness.
- Senior data engineers are important for architectural decisions and team leadership, but may be an over-investment for execution-focused roles.
Prioritize Tool Fluency and Communication Skills
When considering a data engineer candidate, evaluate their hands-on experience with your specific technology stack. A data engineer with proven experience in your orchestration tools, cloud platforms, and data processing frameworks can contribute immediately rather than spending months learning unfamiliar systems.
But communication and documentation skills are equally important. Remote data engineers must articulate complex technical concepts well and maintain system documentation. Make sure anyone you bring aboard your team can handle these tasks.
Partner with Specialized Talent Providers
Look beyond traditional ways to hire. JADA delivers pre-vetted remote data engineers along with integration assistance and performance monitoring. Through remote data engineer hiring, organizations find candidates with industry-specific experience or technical specializations often unavailable in local markets, while providing enhanced cost effectiveness compared to location-based hiring premiums.
Why Companies Choose JADA for Data Engineers
JADA specializes in hiring pre-vetted data engineers from Africa's thriving tech ecosystem and embedding experts into your teams who can start contributing to your data pipelines, warehouses, and analytics platforms immediately.
Our talent identification process focuses on Africa's rapidly expanding technology ecosystem, which has produced world-class engineering talent with strong technical fundamentals and diverse problem-solving approaches. JADA’s data engineers undergo rigorous vetting for modern data stack competencies, including:
- Apache Airflow
- Spark
- dbt
- Snowflake
- All major cloud platforms
The JADA model addresses common concerns about remote work. Our remote data engineers work from our formal offices, providing enterprise-grade infrastructure, direct technical oversight, and comprehensive data security.
Our data engineers are specifically trained for rapid integration with client technical environments and workflows. Rather than extended onboarding periods typical of traditional hiring, our professionals begin contributing to production systems within 2 weeks, compared to 2-4 months for traditional approaches.
Build Your Data Infrastructure Advantage Today
JADA's specialized approach delivers pre-vetted, infrastructure-ready data engineers who integrate seamlessly with existing technical environments and begin contributing immediately to critical system development and optimization initiatives.
Book your consultation today to discover how JADA can provide the data engineering capabilities essential for your organization's success.
Frequently Asked Questions About Hiring Data Engineers
What does a data engineer do?
A data engineer builds infrastructure that collects, processes, and stores data using tools like Airflow, Spark, and Snowflake, enabling analysts and scientists to access clean information for business insights.
What is the hiring process for a data engineer?
Hiring data engineer talent involves defining infrastructure needs, testing practical skills like pipeline building, and evaluating hands-on experience with your tech stack.
What is the salary of a data engineer?
Data engineer salaries range from $98,663 to $174,892 annually, depending on experience level, with remote data engineer positions often offering competitive rates at lower costs.
