Data Engineer: What is the cost of hiring?

What is a Data Engineer?

A Data Engineer is responsible for the systems that help your organization collect and manage large volumes of data. They help create processes to combine data from different sources and make it more accessible for others.

Data Engineers generally work closely with technical and business teams to organize data to support reporting, analysis, forecasting and day-to-day operations.

Why hire a Data Engineer?

Data Engineers can help your team work with data more efficiently by building the pipelines and systems that support reliable access. Their work may reduce time spent on manual data preparation and help teams use consistent, accurate information.

Contributions of a great Data Engineer:

• Faster access to organized and trustworthy data
• Fewer delays in analytics and automation
• Scalable infrastructure to support business growth

Defining your hiring needs for Data Engineers

If your team handles complex systems and needs ongoing support for infrastructure and automation, a full-time role may be the most efficient option. For limited-scope projects, such as building a one-time pipeline or migrating data to the cloud, working with an external consultant may be more practical.

It’s also helpful to define the scope of the role to determine if a Data Engineer is the right job title for your company’s needs. If managing data architecture is a priority, this role may work alongside or report to a Data Architect, while teams focused on modeling and analytics may have the Data Engineer support or collaborate closely with Data Analysts, Business Intelligence Analysts or Machine Learning (ML) Engineers.

What are the types of Data Engineer?

Data Engineers may specialize in different aspects of data architecture or pipeline development. Types of Data Engineers typically include:

• Pipeline Data Engineers: These engineers design workflows that move data from source systems into usable formats. They may build extraction, transformation and loading (ETL) processes that prepare raw information for reports or analytics tools.
• Cloud Data Engineers: In this role, the Engineer manages data platforms running on cloud services such as Amazon Web Services (AWS) or Google Cloud. They may also set up infrastructure and scaling systems to meet performance needs.
• Real-Time Data Engineers: These engineers work with streaming data tools to support applications reliant on live information. Frameworks such as Apache Kafka or Spark Streaming may help Real-Time Engineers address data as it arrives.
• ML Data Engineers: Some positions support artificial intelligence (AI) and ML teams by organizing data pipelines that feed training models. This can involve automating delivery, cleaning large datasets and optimizing system performance to reduce lag or error rates.

When writing your Data Engineer job description, consider including some or all of the following keywords to improve the visibility of your job posting. These are the most popular search terms leading to clicks on Data Engineer jobs, according to Indeed data:

• Data Engineer
• Data
• SQL
• Azure Data Engineer
• Senior Data Engineer
• Python
• Engineer 
• Data science 
• AWS
• Data Engineer
• Software 

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Where to find Data Engineers

Data Engineers can be sourced through tech job boards, data-focused communities, university partnerships and professional networking platforms. Engaging with coding bootcamps, attending data and analytics conferences or connecting with alumni from computer science and engineering programs can help you reach qualified candidates. Posting your role on general and specialized job sites also broadens your visibility to active tech professionals.

To find the right Data Engineer for your business, consider trying out a few different recruiting strategies:

• Internal referrals: Ask your data or engineering teams if they know job seekers who’ve worked with similar tools or architectures.
• Specialized recruiters: Technical recruiters focused on data roles may be connected with engineers experienced in pipeline design, infrastructure management, cloud migration or platform optimization. 
• Open-source and cloud communities: Many Data Engineers contribute to public GitHub projects or engage in vendor-specific forums like AWS re:Post, Databricks Community or Stack Overflow.
• Online job platforms: Posting your job on Indeed can help you connect with candidates searching for Data Engineering roles.

Skills to look for in a Data Engineer

A successful Quality Assurance Analyst candidate will typically have the following skills and competencies, which may be gained through a variety of experiences, training or education:

• Experience building and maintaining extract, transform, load (ETL)  and extract, load, transform (ELT) pipelines using tools such as Apache Airflow, data build tool (dbt) or custom scripts written in Python or structured query language (SQL)
• Proficiency with cloud data services, including Amazon Redshift, Google BigQuery, Azure Synapse or Snowflake
• Experience managing storage, compute resources and security configurations
• Proficiency with database design and optimization, including indexing strategies, partitioning and normalization for relational and non-relational systems
• Knowledge of containerization and orchestration tools, such as Docker and Kubernetes, particularly in environments with multiple services handling data workflows
• Familiarity with distributed processing frameworks like Apache Spark or Kafka, especially in roles supporting real-time or large-scale batch data processing
• Experience implementing data validation and quality checks using libraries, testing frameworks or in-pipeline assertions to catch errors before data is consumed
• Ability to version and document code using Git-based workflows, with an emphasis on maintainability, peer review and structured collaboration across teams

Writing a Data Engineer job description

Now that you know the key skills, salary expectations, popular job seeker search terms and hiring insights for a Data Engineer, you’re ready to write a job description.

A Data Engineer job description typically includes a compelling summary of the role, a detailed list of duties and responsibilities and the required and preferred skills for the position. You may also want to include information about your company culture, benefits and perks to attract candidates to your open role.

Ready to get started? See our full guide for writing Data Engineer job descriptions.

Interviewing Data Engineer candidates

Strong candidates for Data Engineer positions will be confident answering questions about:

• Designing and maintaining pipelines that move data between systems or environments
• Writing and optimizing complex SQL queries for analytics or transformation
• Troubleshooting failures in cloud-based infrastructure or job orchestration tools
• Implementing data quality checks and monitoring solutions at scale
• Collaborating with Data Analysts, Scientists and Software Engineers on shared workflows

Need help coming up with interview questions? Our Data Analyst interview questions are a helpful starting point for hiring a Data Engineer.