In 2025, the role of data engineers is rapidly changing. Many companies still hold onto old ideas about what data engineers do, which can slow down your data team and reduce business impact. To stay competitive, you need to understand when, how, and why to hire data engineer the right way.

This guide helps you build a strong data engineering team that drives your business forward. From defining the role to hiring the perfect candidate, you’ll learn everything needed to succeed with data engineering in today’s fast-evolving landscape.

What is a Data Engineer?

Data engineers are the builders and caretakers of your company’s data infrastructure. They move raw data from external sources into your internal systems. Think of them as the ones who design and maintain the pipelines and plumbing that keep your data flowing reliably.

Their responsibilities include managing data pipelines, setting up and maintaining cloud databases and warehouses, configuring data orchestration tools, and ensuring the smooth deployment of data workflows.

They also handle permissions and security for these systems while optimizing performance and cost. Simply put, data engineers make sure the data your analysts and scientists use is available, clean, and reliable.

What Does a Data Engineer Do in 2025?

The day-to-day role of a data engineer has evolved significantly. With modern tools, many basic ingestion and transformation tasks are automated or handled by self-service tools analysts use themselves.

Today, data engineers focus on:

• Managing and optimizing your core data infrastructure: Ensuring your cloud data warehouse and pipeline tools run efficiently and cost-effectively.
• Building and maintaining custom data ingestion pipelines: Creating pipelines for niche or complex data sources that off-the-shelf tools can’t handle.
• Supporting data analysts with SQL transformation optimization: Collaborating to write efficient, scalable SQL transformations and improving overall pipeline performance.
• Developing non-SQL pipelines: Using languages like Python to enrich data and integrate machine learning workflows.

Data engineers have moved from pure data movers to partners enabling your broader data team to succeed.

Managing and Optimizing Core Data Infrastructure

Your cloud data warehouse and pipeline infrastructure are vital assets. Data engineers monitor job performance, detect bottlenecks, troubleshoot failures, and optimize costs by tuning database schemas, compression, and partitioning.

They also build custom monitoring and alerting systems to maintain high reliability. Maintaining data infrastructure means fewer surprises and ensures your BI and analytics tools run smoothly even as your data grows.

Building and Maintaining Ingestion Pipelines

Many companies integrate common data sources like CRM, web analytics, marketing platforms, and ERP systems using ready-made connectors. However, about 10-25% of data sources can be unique or domain-specific, and this is where data engineers add value by crafting custom ingestion pipelines.

They rely on scalable orchestration platforms to automate these workflows and maintain data accuracy and timeliness. Skilled engineers ensure pipelines are resilient and can adapt as new data sources or business needs arise.

Supporting Data Teams with SQL Transformation Optimization

While analysts use tools like SQL and transformation platforms to model and transform data, data engineers help improve performance and maintainability.

When queries become slow or data models complex, engineers redesign architecture and optimize code to shorten report and dashboard load times. They review analytic workflows to ensure transformation logic is clear and scalable, enabling analysts to focus on insights.

Building Non-SQL Transformation Pipelines

Not all data work fits neatly into SQL. For advanced use cases, data engineers build pipelines in Python, Scala, or other languages. This can include geospatial data enrichment, complex aggregations, or machine learning pipeline integration.

Orchestrating these multi-language pipelines efficiently is critical, and data engineers deploy automation tools to run these workflows reliably in production.

Data Engineer vs. Machine Learning Engineer

It’s important to distinguish the two roles when building your team. Data engineers create and maintain the infrastructure and pipelines that provide clean, usable data. They use tools like Airflow, cloud data warehouses, and scalable code to handle data flow.

Machine learning engineers focus on developing predictive models using frameworks like TensorFlow or PyTorch. They rely on the infrastructure built by data engineers but specialize in training, validating, and deploying machine learning models.

Both roles are essential but serve different parts of the data value chain.

When Does Your Team Need a Data Engineer?

You might think you need data engineers right away, but many organizations start successfully with analysts and scientists using self-serve tools.

Consider hiring data engineers when:

• You have at least 3 data analysts or scientists needing more stable, scalable data pipelines.
• Your BI platform has 50 or more active users whose queries demand reliable data and better performance.
• Your largest tables hit around 1 billion rows, requiring expert tuning and infrastructure oversight.
• You require 3 or more mission-critical custom ingestion pipelines that off-the-shelf solutions cannot meet.

Hiring data engineers before these scale points might lead to underutilized skills, while waiting too long risks slowdowns and frustrated analysts.

Whom Should You Hire?

The best data engineers today love collaboration. They work alongside less-technical colleagues to solve problems and improve workflows.

Avoid engineers who prefer to isolate themselves in backend systems without engaging the broader team. Look for those who enjoy building tools and processes that make analysts and scientists more productive. Consider hiring a remote data engineer to promote better collaboration within your organization.

Strong communication skills combined with deep technical expertise in cloud data platforms, pipeline orchestration, and programming are key traits.

How to Write Job Descriptions for Data Engineers

Crafting compelling job descriptions will help you attract the right talent:

• Background: Explain your company, your data goals, and why this role matters.
• Requirements: List key technical skills and experience, keeping it focused and realistic.
• Responsibilities: Describe day-to-day tasks, such as pipeline management, architecture improvement, and automation efforts.
• Hiring Process: Share the steps candidates will go through to apply and interview.
• Ramp-Up Plan: Outline clear expectations for the first 30, 60, and 90 days, such as onboarding to monitoring, contributing to projects, and delivering pipelines.

Transparent and detailed descriptions help build a strong, diverse candidate pool.

Sample Job Description Example for Data Engineer at Your Company

Job Overview: You will build and maintain reliable data pipelines, optimize cloud data warehouses, and collaborate closely with analytics teams.

Requirements: Experience with Python, Apache Airflow, cloud data warehouse platforms (Snowflake, BigQuery, Redshift), and strong problem-solving skills.

Responsibilities: Develop and monitor ingestion pipelines, tune data infrastructure, support data analysts, and automate data workflows.

Hiring Process: Resume screening, recruiter phone call, technical coding challenge, technical interview with peers, final interview with leadership.

Ramp-Up Plan:

• 30 days: Understand current infrastructure and support monitoring.
• 60 days: Own small pipeline improvements and collaborate with analysts.
• 90 days: Deliver custom ingestion pipelines and optimize performance.

How TaasFlow Can Help

Hiring expert data engineers can be challenging and time-consuming. That’s where TaasFlow steps in. TaasFlow connects you with the top 3% of vetted data engineering professionals within 24-48 hours.

Our flexible subscription model allows you to scale your team on demand with precision, ensuring you get the right talent for your projects without the hassle of traditional recruiting.

With TaasFlow, you get instant access to specialists skilled in cloud data platforms, pipeline orchestration, ELT processes, and more. We help you accelerate your data initiatives with experts who align perfectly with your technical needs and culture. 

Learn about the Pre-Vetting Process Of TaaSFlow

Conclusion

Building a modern data engineering team is essential for data-driven success in 2025. By knowing when to hire, what to look for, and how to attract top talent, you set your business up for fast, reliable data delivery.

Remember, data engineers make your analytics team more productive and your business more agile. Partnering with expert providers like TaasFlow ensures you find the right talent quickly and scale your data capabilities with confidence.

Start building your future-ready data team today with TaasFlow and unlock the full potential of your data.