Introduction 

The AI pilot your company implemented in demo was great, but things went south when it was implemented on actual corporate data. 

Customers’ details didn’t line up between databases, the reports were based on three separate sources and no one knew which numbers to trust. 

This is typically an issue with data engineering, which is why there are so many companies currently recruiting for this position. 

To hire a data engineer, start by defining the data work you need done. Then screen for hands-on skills in SQL, data pipelines, and cloud data platforms, and test candidates on a realistic project before you make an offer. The right hiring model depends on whether the work is a one-time build or something you'll need to run for years.

What Does a Data Engineer Do? 

A data engineer builds and maintains the systems that move, clean, and store your company's data so other people and tools can use it. 

Day to day, that usually means: 

  • Building pipelines that pull data from apps, databases, and outside sources. 
  • Cleaning and combining that data so it's consistent and trustworthy. 
  • Designing data models and warehouses that reports and AI tools can query. 
  • Monitoring pipelines so problems get caught before anyone sees a bad number. 

Data engineers are different from data analysts and data scientists. Analysts and scientists use data to answer questions. Data engineers make sure the data is there, and correct, when they go looking for it.

Why Are Data Engineers in Such High Demand Right Now? 

As companies move AI from experimentation into everyday business operations, the demand for reliable, well-organized data is growing rapidly. 

Gartner's CIO Planning for 2027 research projects that data and analytics will see the highest hiring demand of any IT area, at 56%, followed by cybersecurity at 54%. Gartner's top strategic technology trends for 2027 also center on autonomous AI agents and the controls around them. 

Agents and AI tools can only work with the data they're given. If that data is scattered or messy, the results will be too. Data engineers are the people who fix that.

What Skills Should You Look For in a Data Engineer? 

A skilled data engineer brings together strong technical expertise and practical experience building reliable data systems. Key qualifications include proficiency in SQL, hands-on experience developing data pipelines, and familiarity with the cloud platform your organization uses.

Technical skills 

  • SQL: writing, debugging, and tuning queries on large data sets. 
  • Python or a similar language: for building and automating pipelines. 
  • ETL and ELT: moving data from source systems into a warehouse or lake. 
  • Cloud data platforms: experience with data services on AWS, Azure, or Google Cloud. 
  • Data modeling: designing tables and structures that are easy to query and maintain. 
  • Data quality and security: testing data, handling access, and protecting sensitive fields. 

Skills that are harder to spot on a resume 

  • Explaining technical tradeoffs to people outside IT. 
  • Asking good questions about what the business needs from the data. 
  • Owning a problem until it's fixed, even when the cause is upstream. 

These soft skills matter more as AI takes on routine coding. A data engineer who understands why the business needs a number will build a better pipeline than one who only knows how.

Should You Hire a Full-Time Data Engineer or Bring In Contract Help? 

Hire full time when you need someone to own your data platform long term. Bring in contract help when the work has a clear end date, like a migration or a single AI project.

Model 

Best For 

Speed to Start 

Commitment 

Direct hire 

Owning your data platform and pipelines for years 

Slower 

Long term 

Contract 

Migrations, one-time builds, and project spikes 

Fastest 

Ends with the project 

Contract-to-hire 

A new data role you expect to keep but haven't fully defined 

Fast 

Flexible, with an option to convert 

Many teams use both. A contract data engineer can stand up the pipelines for an AI project, while a permanent hire learns the systems and takes over. For a deeper look at each model, see Contract, Contract-to-Hire, or Direct Hire: Which IT Hiring Model Fits the Role? 

How Do You Screen Data Engineer Candidates? 

Test them on real work. A resume can list every tool in the stack, but it can't show you how someone handles a broken pipeline or a slow query. 

  • Start with a short technical assessment to confirm core SQL and coding skills.
  • Hold a technical interview focused on past projects, the problems they hit, and how they solved them.
  • Give a hands-on challenge that looks like the work they'd do for you.
  • Check communication by having them walk a non-technical person through their solution. 

If you choose to work with a staffing agency, ask how they vet data talent before you ever see a resume. Emergent Staffing, an IT staffing firm in the Twin Cities, Minnesota, places data, cloud, software, and cybersecurity professionals on contract, contract-to-hire, and direct hire terms. 

Our hiring process evaluates hundreds of applicants for each role through online technical assessments, IT challenges, and interviews. For database and data roles, our role-based challenge asks candidates to complete a mini database, ETL, and reporting project, write a stored procedure, and tune a poorly performing query. By the time you meet someone, they've already shown they can do the work. 

How Do You Know If Your Data Team Is Ready for AI? 

Your data team is ready for AI when your data is consistent, easy to find, and owned by someone. Ask these questions before your next AI project starts. 

  • Do your main systems agree on basic facts, like customer names and product codes? 
  • Can someone explain where each important number in a report comes from? 
  • Is there a named owner for each major data source? 
  • Can your pipelines handle more data and more users without breaking? 
  • Do you know who can access sensitive data, and is that access logged? 

If you answered no to two or more, plan data engineering work before AI work. For more on where data roles fit on your team, see our guide to IT department structure. If your data lives in the cloud, Cloud Strategy Is Getting More Complex. Does Your Team Have the Skills to Keep Up? covers the skills that go with it. 

Frequently Asked Questions 

How much does it cost to hire a data engineer? 

It depends on seniority, location, and whether you hire full time or on contract. Contract rates are higher per hour, but you only pay for the time you need, with no benefits or long-term commitment. 

How long does it take to hire a data engineer? 

Hiring on your own can take months once you count sourcing, interviews, and notice periods. Working with a staffing firm is usually faster. Emergent's median time to hire across contract and direct hire roles has been 15 days. 

Should you hire a data engineer or a data scientist first? 

Most companies should hire a data engineer first. A data scientist needs clean, reliable data to do their job, and a data engineer is the one who builds it. 

Do you need a data engineer if you already have data analysts? 

Often, yes. If your analysts spend much of their time fixing and combining data before they can analyze it, a data engineer will free them up for the work you hired them to do. 

How Emergent Staffing Can Help 

Hiring a data engineer is easier when every candidate you meet has already proven their skills. Emergent Staffing helps IT leaders find data engineers through our contract and direct hire IT staffing services, with technical vetting built into every search. 

Whether you need someone to clean up your data before an AI project or a long-term owner for your data platform, we'll help you find the right fit. 

Talk to us about your Hiring Needs