How To Hire A Data Engineer

Data Engineers are the backbone of any organization’s data infrastructure. They design, build, and maintain systems that allow teams to collect, store, and analyze data efficiently and securely. Whether your company is scaling its analytics capabilities or launching new data-driven products, hiring the right Data Engineer is a critical step toward unlocking business intelligence and long-term growth.

Understanding The Role & Responsibilities

Data Engineers develop and manage the architecture that powers data pipelines and analytics systems. They ensure data flows reliably from source to destination: clean, organized, and accessible.

Common responsibilities include:

• Designing, building, and maintaining scalable data pipelines
• Creating and managing data warehouses, lakes, and ETL/ELT systems
• Cleaning, transforming, and validating raw data for accuracy and usability
• Collaborating with analysts, scientists, and product teams to meet data needs
• Managing data integration from APIs, third-party systems, and internal tools
• Ensuring data security, governance, and compliance
• Monitoring performance, resolving bottlenecks, and optimizing infrastructure

Data Engineers often work alongside Data Analysts, Scientists, and Product Managers to ensure data is accessible, reliable, and actionable.

Skills To Look For In A Great Data Engineer

Data Engineering requires a blend of software development expertise, data architecture knowledge, and a strong understanding of business needs.

Here are key skills to prioritize:

Proficiency In Programming Languages

Python and SQL are essential. Look for familiarity with Scala, Java, or Go, depending on your tech stack.

Experience With Data Pipelines & ETL Tools

Candidates should be fluent in building pipelines using tools like Apache Airflow, dbt, Talend, or custom-built solutions.

Cloud Platform Knowledge

Cloud-based data infrastructure is the norm. Look for experience with AWS (Redshift, Glue), GCP (BigQuery, Dataflow), or Azure (Synapse, Data Factory).

Data Warehousing & Storage Expertise

Strong candidates will have experience designing schemas and managing platforms like Snowflake, BigQuery, Redshift, or Databricks.

Database Management

Knowledge of relational databases (PostgreSQL, MySQL) and NoSQL systems (MongoDB, Cassandra) is often required.

Data Modeling & Architecture

Understanding star/snowflake schemas, normalization, and data lifecycle best practices is essential for scalable infrastructure.

Problem Solving & Collaboration

Data Engineers must understand stakeholder needs, troubleshoot data quality issues, and collaborate across teams.

Other Transferable Titles

If you’re expanding your search, consider candidates with titles such as:

ETL Developer

Focused on data pipeline development and transformation processes. They often have deep experience in building scalable ingestion systems.

Data Infrastructure Engineer

A more specialized title emphasizing cloud, storage, and system performance.

Database Developer or Administrator

These professionals may have strong SQL skills, schema design experience, and performance tuning abilities.

Backend Software Engineer

Engineers who’ve worked on data-heavy backend services may be well-suited for data engineering roles, especially in early-stage companies.

Analytics Engineer

More focused on transforming data for end users, but often overlap in ETL and pipeline management.

Interview Questions

Here are some questions to guide your interview process:

1. Describe a data pipeline you built from scratch. What technologies did you use, and what challenges did you face?
2. How do you approach optimizing data processing for performance and cost?
3. What data storage solutions have you worked with, and how did you choose between them?
4. How do you ensure data quality and integrity across your pipelines?
5. Describe your experience with Airflow, dbt, or another orchestration tool.
6. How do you manage schema changes and data versioning?
7. What’s your experience with cloud data platforms (e.g., Snowflake, Redshift, BigQuery)?
8. Tell me about a time you worked closely with analytics or product teams to deliver a data solution.

These questions help you uncover real-world experience, technical depth, and collaboration skills.

Evaluating & Making The Final Decision

When assessing candidates, focus on:

• Alignment with your tech stack and cloud environment
• Experience building and maintaining robust, scalable data systems
• Ability to collaborate with analytics, product, and engineering teams
• Comfort troubleshooting issues and improving data workflows
• Security and compliance awareness, especially if handling sensitive data

If possible, assign a short take-home challenge (e.g., designing a basic pipeline or optimizing a SQL query). Evaluate for clarity, documentation, and design decisions, not just correctness.

For reference checks, ask about reliability, communication, and how the candidate handled scaling or failure scenarios.

Partner With Premier

Hiring a Data Engineer is more than filling a technical role. It’s about building the infrastructure that powers your decision-making and innovation. At Premier, we connect you with experienced, vetted Data Engineers who can help your organization scale with confidence.

Whether you’re building your first data pipeline or optimizing enterprise systems, we’ll help you find the right technical talent for the job.

Get in touch with Premier today and start building your data foundation.