What Does an Azure Data Engineer Do? Skills, Responsibilities, and How to Hire One

What do they actually do?

An Azure data engineer’s day-to-day work revolves around:

• Building and managing data pipelines with Azure Data Factory and Azure Databricks.
• Optimizing storage and databases like Azure SQL and Synapse Analytics for speed and efficiency.
• Maintaining security and compliance to reduce the risk of data breaches.
• Collaborating with data scientists to support AI and machine learning initiatives.

Who needs an Azure data engineer professional?

Businesses that rely on real-time insights and massive datasets, like finance, healthcare, e-commerce, and AI-driven companies, often hire Azure data engineers. 

With 74.5% of data engineering job postings requiring Microsoft Cloud expertise, Azure is the clear leader in enterprise cloud solutions.

What is an Azure data engineer’s salary?

Salaries vary based on location and cost of living of popular countries to hire in. In the US, an Azure data engineer commands a high salary:

• US: $87K–$177K (our Data Roles Salary Guide)

It’s a similar situation in countries with a moderate to high cost of living:

• Canada: $72K–$135K (Levels.FYI)
• UK: $64K–$175K (Levels.FYI)
• Singapore: $74K–$159K (Levels.FYI)

Nearshore countries like those in LatAm typically have wages up to 52% lower than those of the US:

• LatAm region average: $42K–$84K (our Data Roles Salary Guide)

How are they different from AWS and GCP engineers?

While AWS and GCP have their own data engineering roles, Azure’s tight integration with Microsoft tools like Power BI and SQL Server makes it the top choice for businesses already invested in the Microsoft ecosystem. 

If your company runs on Windows, Office 365, or SQL Server, an Azure data engineer is the best fit.

The technical must-haves

A great Azure data engineer knows how to move, store, and process data at scale using Microsoft’s cloud tools:

• Azure Data Factory: Automates data movement between different sources.
• Synapse Analytics: Handles big data warehousing for lightning-fast queries.
• Databricks and Spark: Powers large-scale ETL pipelines and machine learning workflows.
• SQL and Python: The bread and butter of data manipulation.
• Data governance and security: Provides compliance with industry standards.

Soft skills also matter

Even the best technical skills won’t mean much if an engineer can’t collaborate effectively. Companies need Azure data engineers who:

• Solve problems efficiently: Cloud environments are complex, and things break. Quick thinking is key.
• Communicate clearly: Explaining data concepts to non-technical stakeholders is crucial.
• Thrive in remote teams: Many Azure engineers work across time zones, so strong asynchronous collaboration skills are essential.

Why certification matters

Hiring an engineer with the Microsoft Certified: Azure Data Engineer Associate credential means you’re getting a tested expert. 

This certification validates their ability to design and implement data solutions using Azure. It’s a critical factor when building a reliable, scalable cloud infrastructure.

The case for in-house hiring

Hiring an in-office Azure data engineer allows for direct collaboration with internal teams, making it easier to integrate into company culture. However, it comes with significant drawbacks. 

Salaries, office expenses, and benefits increase overall costs, and the local talent pool can be limited, making it difficult to find highly skilled engineers—especially for smaller businesses and startups.

Why remote hiring makes sense

As Azure data engineering is primarily computer-based work, engineers can efficiently work from anywhere, opening your business up to a world of talent.

The benefits of hiring offshore Azure data engineers include:

• Access to global talent rather than competing for a limited supply of US-based engineers.
• Lower costs while maintaining high-quality expertise. Azure engineers in countries with lower living costs than the US offer competitive skills at significantly lower salaries.
• Same-day collaboration or round-the-clock operations depending on time zone alignment with the US and preferences.

For companies hesitant about hiring full-time remote staff, hiring freelance data engineers can be a flexible alternative for short-term projects.

Step 1: Define your requirements

Before diving into the hiring process, outline exactly what your Azure data engineer needs to do and what tech stack you’re currently using. Consider:

• The specific Azure tools they should be proficient in, such as Azure Data Factory, Synapse Analytics, or Databricks.
• Whether they’ll be working on data pipelines, data warehousing, or machine learning models.
• If experience in real-time data processing, compliance, or security is required.
• The level of experience needed: mid-level for hands-on development or senior for strategic cloud data architecture.
• Whether the role will be full-time, contract, or project-based.

Step 2: Set a budget

Before starting the hiring process, it’s important to define a realistic budget based on market rates and business needs. Here’s how to calculate what you should expect to pay:

1. Determine the experience level you need: Junior, mid-level, or senior Azure data engineers have different salary expectations.
2. Factor in hiring overheads: Beyond salary, consider expenses like recruitment fees, onboarding, and benefits.
3. Decide between in-house and remote hiring: Salaries vary significantly by location, so knowing where you plan to hire helps estimate costs.

Want to see exactly how much you could save by hiring in LatAm? Use our LatAm savings calculator.

Step 3: Choose a sourcing method

There are several ways to find Azure data engineers:

• Freelance platforms: Websites like Upwork and Toptal offer flexibility but require thorough vetting.
• Job boards: Posting on LinkedIn, Indeed, or Glassdoor helps reach a broad talent pool but, like freelance platforms, requires time-intensive screening.
• Recruitment agencies: Specialized recruitment firms handle screening and assessments, delivering a short list of pre-vetted candidates ready for you to interview. 

For companies that want a fast, low-effort hiring process, partnering with a recruitment agency is the most efficient option, especially if you hire outside the US. 

Not only do agencies take care of sourcing, screening, and technical evaluations, but they also assess English proficiency and company culture fit. Plus most agencies that specialize in offshore hiring will handle all the compliance and payroll logistics. 

Step 4: Write a strong job description

If you decide to go it alone and hire directly, a clear job description helps attract the right candidates. Be specific about:

• Technical requirements: For example: “Seeking an Azure Data Engineer with Azure Data Factory expertise and ETL pipeline experience.”
• Certifications: Microsoft Certified: Azure Data Engineer Associate is a plus, but remember that sometimes experience trumps certification.
• Work expectations: Clarify if the role is remote, hybrid, or in-office, and outline collaboration expectations.

Step 5: Resume screening and technical assessments

Resumes only tell part of the story, while technical assessments help validate skills. Key areas to test include:

• Building ETL pipelines in Azure Data Factory.
• SQL performance optimization.
• Cloud security best practices for Azure data environments.
• Hands-on Azure problem-solving scenarios.

Step 6: Interview and make an offer

Beyond technical knowledge, a great Azure data engineer should:

• Explain complex cloud architectures in simple terms.
• Have real-world problem-solving experience with cloud data challenges.
• Communicate effectively with data scientists, engineers, and business stakeholders.

For a list of the best questions to ask, see our article “Want to Hire the Best Data Engineer? Ask These Essential Questions.”

Once you find the right fit, make a competitive offer with clear contract terms and benefits.

Step 7: Onboarding and retention

A smooth onboarding process sets engineers up for success. Best practices include:

• Providing access to documentation, cloud environments, and collaboration tools.
• Defining clear expectations for projects, meetings, and communication.
• Encouraging continuous learning with Azure certifications and upskilling opportunities.

Following these steps will ensure you hire an Azure data engineer who not only meets technical expectations but also enhances your cloud data operations from day one.