DATA ENGINEER JOB DESCRIPTION

Search our latest jobs

Data Engineers design, build and maintain the systems that help organizations collect, transform, store and use data effectively.

They play a critical role in modern data, analytics and AI teams, creating the pipelines, platforms and processes that make reliable data available to the people and systems that need it.



This Data Engineer job description guide is designed to help employers define the role clearly and help candidates understand the responsibilities, skills and experience commonly expected in data engineering jobs.

A Data Engineer is responsible for building and maintaining the infrastructure that moves data through an organization.

This can include data pipelines, data warehouses, data lakes, ETL and ELT processes, cloud data platforms, data models, APIs and integrations between business systems.

In many organizations, Data Engineers work closely with data analysts, analytics engineers, data scientists, machine learning engineers, software engineers and business stakeholders. Their work helps teams access clean, reliable and well-structured data for reporting, analytics, machine learning and decision-making.



The exact responsibilities can vary depending on the organization’s size, data maturity, technology stack and whether the role sits closer to engineering, analytics, platform, architecture or AI.

The responsibilities of a Data Engineer will depend on the business, industry and technical environment, but typical duties include:

• designing, building and maintaining data pipelines
• developing ETL and ELT processes
• integrating data from multiple internal and external sources
• building and maintaining data warehouses, data lakes or lakehouse environments
• writing, testing and optimizing SQL queries
• using Python or other programming languages to automate and transform data
• improving data quality, reliability and performance
• supporting analytics, reporting and business intelligence teams
• preparing data for machine learning, AI or advanced analytics use cases
• working with cloud platforms such as AWS, Azure or Google Cloud
• implementing orchestration, monitoring and alerting for data workflows
• supporting data governance, documentation and metadata management
• working with data architects to design scalable data models
• collaborating with analysts, data scientists, engineers and business teams
• troubleshooting pipeline failures and performance issues
• supporting security, privacy and compliance requirements 



A strong Data Engineer usually combines technical engineering skills with problem-solving, communication and an understanding of how data supports business goals.

Common Data Engineer skills include:

• SQL
• Python
• ETL and ELT development
• data pipeline design
• data warehousing
• data lakes and lakehouse architecture
• data modeling
• data integration
• data quality management
• cloud platforms
• orchestration tools
• version control
• CI/CD
• API integration
• performance optimization
• testing and monitoring
• documentation
• stakeholder communication

The exact skill set will depend on the role. A Cloud Data Engineer may need deep AWS, Azure or Google Cloud experience. An Analytics Engineer may need stronger dbt, SQL modeling and reporting knowledge. A Data Platform Engineer may need more infrastructure, DevOps and platform experience.

Data Engineers work with a wide range of tools depending on the organization’s data environment.

Common tools and technologies include:

• Programming and querying: SQL, Python, Scala, Java
• Cloud platforms: AWS, Azure, Google Cloud
• Data warehouses and platforms: Snowflake, Databricks, BigQuery, Redshift, Synapse
• Data processing: Spark, Kafka, Flink
• Transformation and modeling: dbt, SQL, Python
• Orchestration: Airflow, Dagster, Prefect
• Integration and ELT: Fivetran, Matillion, Informatica, Talend
• DevOps and infrastructure: Docker, Kubernetes, Terraform, Git, CI/CD
• BI and analytics: Power BI, Tableau, Looker
• Governance and quality: Great Expectations, Collibra, Alation, Monte Carlo

Employers should avoid turning the job description into a long shopping list of every tool in the stack. It is usually better to separate essential experience from desirable tools.

Data Engineers can move in several directions as their experience grows.

Some progress into Senior Data Engineer, Lead Data Engineer, Principal Data Engineer or Head of Data Engineering roles. Others move toward data architecture, platform engineering, analytics engineering, machine learning engineering or data leadership.

A typical career path may include:

Junior Data Engineer

Data Engineer

Senior Data Engineer

Lead Data Engineer

Principal Data Engineer

Data Architect

Head of Data Engineering

Head of Data

Career progression often depends on technical depth, cloud experience, communication skills, ownership of production systems and the ability to work with stakeholders across the business.

Use this section as a template.

Data Engineer Job Description Template

We are looking for a Data Engineer to design, build and maintain reliable data pipelines and platforms that support reporting, analytics and business decision-making.

The successful candidate will work closely with data analysts, data scientists, software engineers and business stakeholders to make sure data is accurate, accessible and well structured.

You will be responsible for developing ETL and ELT processes, integrating data from multiple sources, improving data quality and supporting the development of scalable cloud-based data solutions.

Key Responsibilities

Design, build and maintain data pipelines and workflows.

Develop ETL and ELT processes across structured and unstructured data sources.

Build and optimize data models, warehouses, lakes or lakehouse environments.

Write clean, efficient SQL and Python code.

Work with cloud platforms such as AWS, Azure or Google Cloud.

Improve data quality, reliability and performance.

Support analytics, reporting, machine learning and AI use cases.

Monitor, troubleshoot and resolve pipeline issues.

Collaborate with data analysts, data scientists, engineers and business stakeholders.

Document data processes, definitions and technical decisions.

Required Skills and Experience

Strong SQL skills.

Experience with Python or another programming language.

Experience building data pipelines or ETL/ELT processes.

Knowledge of data warehousing, data lakes or modern data platforms.

Experience with cloud platforms such as AWS, Azure or Google Cloud.

Understanding of data modeling and data quality principles.

Experience with version control and modern development practices.

Strong problem-solving and communication skills.

Desirable Skills

Experience with Snowflake, Databricks, BigQuery, Redshift or Synapse.

Experience with Airflow, dbt, Spark, Kafka, Fivetran or Matillion.

Experience supporting BI tools such as Power BI, Tableau or Looker.

Knowledge of CI/CD, Docker, Kubernetes or Terraform.

Experience supporting machine learning, AI or advanced analytics use cases.

Understanding of data governance, privacy and security requirements.

Hiring a Data Engineer can be challenging because the role is broad and the skills required can vary from business to business. Before starting the recruitment process, employers should be clear on what they need the person to achieve.

Useful questions to ask include:

• Are you hiring someone to build new data pipelines or improve existing ones?
• Do you need experience with a specific cloud platform?
• Will the person work closely with analysts, data scientists or business stakeholders?
• Is the role focused on data warehousing, data lakes, analytics engineering or AI enablement?
• Do you need a permanent employee, contractor or project-based specialist?

At KDR Talent Solutions, we help businesses define Data Engineer roles properly, understand the market and connect with candidates who match the technical requirements and long-term goals of the team.

Data Engineer compensation in the US can vary significantly depending on location, seniority, industry, technical stack, working model and whether the role is permanent or contract.

Employers should benchmark compensation before going to market, especially for roles requiring cloud platform experience, Databricks, Snowflake, MLOps, streaming data, AI-ready infrastructure or senior technical leadership.

Candidates will often consider the full package, not just base salary. Remote flexibility, bonus, equity, healthcare benefits, learning opportunities, technical ownership and the quality of the data environment can all influence whether a role is attractive. KDR can support employers with compensation benchmarking and market insight before a search begins.

Data Engineer Interview Questions

Employers may ask Data Engineer interview questions covering technical skills, problem-solving, data modeling, cloud platforms, communication and real-world delivery.

Example interview questions include:

Can you describe a data pipeline you have built or maintained?

How do you approach data quality issues?

What is the difference between ETL and ELT?

How would you design a scalable data warehouse or lakehouse?

How do you optimize a slow SQL query?

What orchestration tools have you used?

How do you monitor and troubleshoot pipeline failures?

How have you worked with data analysts or data scientists?

What cloud data platforms have you worked with?

How do you document data pipelines and definitions?

How would you prepare data for machine learning or AI use cases?

How do you handle security, privacy or compliance requirements? 

Our streamlined recruitment process ensures you hire the best talent quickly and effectively.