Data management is one of today's most critical business function. Without a solid grasp of what data management entails, organizations can't use data effectively. So, businesses look to ETL developers and data engineers for everything from data processing and management basics to regulatory compliance and the overall processes that help businesses use data to steer organizational decisions.

5 key takeaways from this post:

  • ETL developers primarily concentrate on extract, transform, load processes, while the scope of a data engineer's role covers the entire data pipeline and infrastructure.

  • ETL developers work with ETL tools and techniques and are sometimes self-taught or hold certain data management certifications, whereas data engineers require a much more diverse skillset that includes programming, database management, and Big Data technologies and often possess a bachelor's degree.

  • An ETL developer's role handles structured data in predefined schemas. A data engineer, on the other hand, often works with both structured and unstructured data from multiple disparate sources.

  • Data engineers have a significant hand in designing, building, and maintaining data architecture, while ETL developers usually work within established infrastructures.

  • Data engineers' skillsets often are comprised of ETL developer skills, while their extended skillsets potentially offer a wider range of career opportunities.

While their functions may seem similar at first glance, ETL developers and data engineers serve distinct purposes within the data management ecosystem. Knowing and understanding these differences is essential for any business that wants to optimize its data workflows and for data professionals who want to follow a certain career trajectory.

In this post, you'll learn the most significant differences between ETL developer and data engineer roles, including how these roles are defined, the responsibilities of each role, each role's required skillset, and the tools these roles use to be successful.

Table of Contents

Role Definition and Responsibilities

To learn the differences between ETL developers and data engineers, let's look first at how these roles are defined, their core duties and responsibilities, and some typical tasks each role might run into on an average day on the job.

What is an ETL Developer?

ETL stands for extract, transform, and load. These are the three main activities that occur during data integration.*

Here's a quick refresh of what happens in each stage:

  • Extract: Pulling data from various sources within an organization, such as customer data from CRM systems, website analytics from search engines, or internal transactional and analytical databases.

  • Transform: Manipulating the extracted data into a format suitable for business stakeholders. This may include converting currency formats, summing fields, or grouping data by specific categories.

  • Load: Making the transformed data available in the organization’s database or data warehouse.

An ETL developer is an expert in the ETL process. Their objective is to move data as accurately and efficiently as possible while preserving its integrity and reusability from the source system to the target systems.

Core duties and activities of an ETL developer daily include:

  • Design, development, and maintenance of ETL processes.

  • Data extraction from databases, APIs, and flat files.

  • Transformation of the data to the required format of the target system through cleaning, aggregation, and normalization.

  • Loading transformed data into data warehouses or other storage solutions.

  • Ensuring data quality and consistency at every point throughout the ETL process.

  • Monitoring and troubleshooting ETL jobs to ensure they run smoothly and efficiently.

  • Validating and cleansing data to ensure accuracy and integrity.

An ETL developer may integrate data from multiple sales systems into a unified warehouse, providing different insights into sales performance. Another example might be the ETL processes themselves: aggregating and cleaning customer data from various sources for unified customer views on which a marketing analysis depends.

*Note: In some data integration workflows, the T and L are transposed to create an Extract, Load, and Transform workflow.

What is a Data Engineer?

Data engineers work on developing and supporting the infrastructure necessary for generating, collecting, storing, and processing data. They design and build data pipelines that manage data flows from hundreds of sources to storage and processing systems. Data engineers ensure that the data is available, good quality, and ready for analysis by data scientists and analysts.

Data engineers are divided into three categories depending on the type of tasks they perform.

A data engineer generalist:

  • Works on smaller teams and handles end-to-end intake, collection, and processing of data.

  • Possesses a broad skillset but doesn't have a lot of experience in systems architecture.

  • A data engineering generalist might work on dashboard creation for a small Ecommerce store to display current inventory and forecast purchase trends.

Pipeline-centric data engineers:

  • Work on a data analytics team that handles complex data science projects distributed across systems.

  • Are commonly found in midsize companies and larger businesses.

  • A pipeline-centric data engineer might develop a solution for a regional Ecommerce business that facilitates search and analysis of inventory metadata and helps predict future store trends.

A database-centric data engineer:

  • Executes, maintains, and populates analytics databases for large organizations.

  • Works with data pipelines, preps and optimizes databases for analysis, and creates schemas using various ETL techniques.

  • A database-centric data engineer could be responsible for building an analytical database for a large national Ecommerce retailer, including writing the code that transfers data from the retailer's main application database(s) to the database decision makers will use for analytics.

Typical responsibilities and day-to-day executions of a data engineer include:

  • Design, build, and maintain scalable data pipelines.

  • Enable and ensure data collection from various sources and loading it to a data lake, data warehouse, or any other form of storage.

  • Implement frameworks for processing large data volumes.

  • Collaborate with data scientists and analysts in understanding data needs and offering the appropriate infrastructure.

  • Monitor and optimize data pipeline performance and reliability.

  • Ensure data security and compliance with data privacy regulations.

  • Maintain and update data infrastructure to adapt to new technologies and business needs.

A data engineer could design a real-time data pipeline that would serve in streaming IoT device data to a data lake for instant processing and analysis. Another example would be establishing a batch processing system to process large volumes of transactional data overnight to report back on the data.

ETL Developer vs. Data Engineer: Technical Skills, Soft Skills, and Other Expertise

Let's look into the main skills that separate ETL developers and data engineers.

ETL Developer Skills

ETL developers and data engineers have a blend of technical and soft skills, but the specific skills required of these roles differ.

An ETL developer should have these technical skills:

  • SQL: Proficiency in SQL is essential for ETL developers, as it is used extensively for querying and manipulating data.

  • ETL tools: Familiarity with ETL tools such as Informatica, Oracle, or Talend.

  • Data warehousing: Understanding data warehousing concepts and architectures is important for designing effective ETL processes.

Soft skills are important for ETL developers, too, such as:

  • Problem-solving: ETL developers must be adept at troubleshooting and resolving issues that arise during the ETL process.

  • Attention to detail: Ensuring data accuracy and consistency requires meticulous attention to detail.

  • Analytical thinking: The ability to analyze data requirements and design appropriate ETL solutions is essential.

Recommended Reading: 5 Must-Have ETL Development Tools

Data Engineer Skills

Technical skills required of data engineers include:

  • Programming languages: Proficiency in Python, Java, SQL, C#, Scala, or other scripting languages.

  • ETL tools and methods: Understanding of basic to advanced data integration and data transformation methods, ETL processes, and ETL tools like Talend, Snowflake, and Informatica.

  • Business intelligence (BI) solutions: A working understanding of popular BI tools, such as Tableau, Looker, or Power BI, for dashboard creation and data visualization.

  • Big data technologies: Knowledge of big data technologies like Hadoop, Spark, and Kafka is important for handling large-scale data processing.

Advanced data engineers also need to know how to work with:

  • Data warehousing: Extensive knowledge and understanding of data modeling, schema design, and datalake tools, such as Hadoop, and familiarity with data warehouse architecture and its tools, such as Amazon Redshift, Snowflake, or Google BigQuery.

  • Database systems (and database management): Experience with relational and non-relational databases, solid understanding of database performance optimization, and efficient table schema creation for designing data storage solutions. They may even come from a software engineer background.

Recommended Reading: What is a Data Engineer and How Do I Become One?

While technical skills are a must, data engineers should have a mix of soft skills, too.

Some of the soft skills most beneficial for data engineers include:

  • Project management: Data engineers often work on complex projects that require strong project management skills to ensure timely and successful delivery.

  • Collaboration: Effective collaboration with data scientists, data analysts, and other stakeholders is crucial for understanding data needs and delivering the right solutions.

  • Critical thinking: The ability to evaluate different approaches and technologies to design efficient data pipelines is important.

Machine Learning

Machine learning (ML) is typically the domain of data scientists or machine learning engineers, but it's beneficial for ETL developers to at least have a working understanding of ML. Data engineers, on the other hand, must prepare data for machine learning platforms and be able to obtain and understand the insights the data provide.

Collectively, these are the skills data engineers use to help support decision-making in organizations that require data-driven insights. But this is only part of how these two data professionals conduct their roles—like most any position, these roles have certain required tools.

Tools and Technologies Used by ETL Developers vs. Data Engineers

By now, you might have picked up on some of the crossover between these two data roles, but you probably also have a good idea of just how different these roles are.

A good analogy to further explain the commonalities and differences between ETL developers and data engineers is to compare boat mechanics with RV mechanics:

  • Both are mechanics.

  • Both use their mechanical knowledge and a set of tools.

  • Both use their knowledge and tools to work on recreational craft.

When comparing ETL developers and data engineers, we know both roles work with data—in fact, within the same organization, it's highly likely both roles may even touch or work with the same data. An ETL developer and a data engineer may even have some overlap in the tools they use or their skillsets. There, for the most part, is where the similarities end.

ETL Developer Tools

Some of the most common tools ETL developers use are:

  • An iPaaS data integration platform with more than 200 different connectors. is especially designed for Ecommerce businesses.

  • Informatica: A widely used ETL tool known for its robustness and scalability.

  • Talend: An open-source ETL tool that offers a wide range of data integration and transformation capabilities.

  • Snowflake: A cloud-based data warehousing and analytics platform that provides a centralized repository for storing, accessing, and analyzing large volumes of structured and semi-structured data.

How do these tools stack up for functionality, user-friendliness, and capabilities?

Side-by-Side Comparisons of ETL Developer Tools

In side-by-side comparisons:

  • Informatica vs. Talend: Informatica is known for its enterprise-level capabilities and strong support, while Talend offers a cost-effective, open-source solution with extensive community support.

  • Talend vs. Snowflake: While Talend shines as a comprehensive data integration and ETL platform, Snowflake distinguishes itself as a cloud-native data warehousing solution optimized for scalable analytics.

  • Talend vs. Talend offers a broad range of data integration capabilities for complex enterprise needs, whereas focuses on providing a user-friendly, no-code approach for simpler ETL tasks and API integrations.

  • Informatica vs. Informatica caters to large-scale, enterprise-level data integration with advanced features, but targets smaller businesses (most notably, Ecommerce stores) and non-technical users with its simplified, cloud-based ETL and data pipeline solutions.

Recommended Reading: Hevo Data vs. Talend vs. Key Features and More

Data Engineer Tools

Data engineers have their own toolbox. Data engineers might use one of (or a combination of) the following data engineering tools:

  • Apache Hadoop: A framework for distributed storage and processing of large data sets.

  • Apache Spark: A fast, in-memory data processing engine that supports batch and streaming data.

  • Apache Kafka: A distributed streaming platform used for building real-time data pipelines.

  • Apache Airflow: A workflow automation tool used to orchestrate complex data pipelines.

Side-by-Side Comparisons of ETL Developer Tools

In side-by-side comparisons:

  • Hadoop vs. Spark: Hadoop is ideal for batch processing of large data sets, while Spark excels in both batch and real-time processing.

  • Kafka vs. Airflow: Kafka is used for real-time data streaming and ingestion, whereas Airflow is used for scheduling and managing complex workflows.

  • Hadoop vs. Airflow: Hadoop is designed for distributed storage and Big Data processing, while Airflow specializes in orchestrating and scheduling complex data workflows and pipelines.

  • Kafka vs. Spark: Apache Kafka excels at real-time data streaming and message queuing, whereas Apache Spark is optimized for large-scale data processing and advanced analytics, including machine learning tasks.

Knowing their varied skillsets and the types of tools these two data integration professionals have in their respective toolboxes, let's look at how they both use that expertise to contribute to the growth of—and make a real difference in—their organizations.

ETL Developer vs. Data Engineer: Organizational Contributions

Data engineers and ETL developers use their depth of knowledge, technical and soft skills, and suite of data tools to contribute a lot to today's data management landscape.

ETL Developer Data Management Contributions

ETL developer contributions include:

  • Data integration, which requires:

    • Data extraction from disparate sources

    • Data cleansing

    • Data validation

    • Data transformation into a cohesive, unified format suited for data analysis

    • Data enrichment

  • Consistent, quality data, which calls for:

    • Pre-defining data standards

    • Ensuring data adheres to the organization's standards

    • Addressing anomalies or debugging

    • Handling missing or empty data values

    • Improving data reliability

ETL developers also offer business intelligence and reporting support with the above contributions. Clean, consistent, reliable data helps analysts provide accurate reports that deliver the insights needed by decision-makers.

Data Engineer Data Management Contributions

Contributions from data engineers to the data processing, storage, and management industry include:

  • Data pipeline creation and management, which requires:

    • Designing and developing pipelines that streamline dataflows from source apps and systems to processing and storage platforms.

    • Ensuring scalable, efficient pipelines that can handle vast libraries of data.

  • Accessible, usable data, which entails:

    • Building strong infrastructure to house data

    • Ensuring data is readily accessible for analysis

  • Data workflow automation, which offers:

    • Streamlined processes and efficiency improvements for machine learning model (MLM) integration within data pipelines

    • Optimized data storage to reduce an organization's overall costs of adopting automated work- and dataflows, thereby improving data performance and allowing the aforementioned data infrastructure to scale with the company's needs.

ETL Developer & Data Engineer Career Paths and Job Opportunities

If you want to break into one of these careers, researching prerequisites, career trajectories, current openings, and future potential can help you decide the right path.

ETL Developer Careers

The typical path of an ETL developer generally begins after they've completed an information technology (IT), computer science, or other related degree program. 

Pursuing a degree provides individuals with the fundamental knowledge required for success in the field, including:

  • Data orchestration

  • Databases and their infrastructure

  • Data warehousing

  • Programming languages and related concepts

With a degree, a graduate can pursue entry-level job descriptions, such as junior ETL developer. As they grow in their roles, they'll take on more responsibilities and transition into more senior roles, such as ETL architect or data integration manager.

ETL developer jobs like ETL architect are reserved for ETL developers who have a tight grasp on the complexities of ETL tools. 

Some ETL developer roles may even be responsible for designing upcoming ETL solutions. Data integration managers oversee entire teams of ETL developers and monitor the flow of raw data from multiple data sources during integration projects.

Mid-senior to senior-level ETL developers often make the move into data analytics or business intelligence, and use their data management experience to support various levels of the decision-making process.

Data Engineer Careers

A data engineer, on the other hand, might start climbing the career ladder as a software developer. As they advance, they might foray into database administrators or lead data engineer and oversee data mapping or data infrastructure development and maintenance. 

Data engineering managers oversee entire data engineering teams and related projects. A data engineering manager might specialize in Big Data, real-time data processing, or engineering data for the cloud.

 In fact, as cloud adoption continues to grow, skilled data engineers can find rewarding careers applying their knowledge and experience to cloud data.

ETL Developers vs. Data Engineers: Similarities, Differences, and Career Paths

To recap:

  • ETL developers work more with specific ETL tools and processes, while data engineers use a broader range of tools and technologies to design scalable data pipelines.

  • Both roles require strong technical skills and a deep understanding of data management principles. However, ETL developers are more specialized in ETL processes and tools, whereas data engineers have a broader focus on data infrastructure and pipeline development.

  • ETL developers focus on the ETL process, ensuring data quality and consistency, while data engineers build and maintain the data infrastructure that supports data accessibility and usability. Both roles are crucial for harnessing the power of data and the overall picture it paints in the world of decision-makers.

ETL developers and data engineers have distinct yet complementary roles within the data management ecosystem, and understanding the differences between these roles is important for individuals who want to follow a specific career path and for organizations that need these professionals on their teams.

ETL Developers, Data Engineers, and is a powerful platform that bridges the gap between ETL developers and data engineers, offering comprehensive data integration and pipeline management solutions. 

Whether you're an ETL developer looking to streamline your ETL processes or a data engineer seeking robust data pipeline tools,'s features and capabilities can help you and your organization succeed. Explore today and take your data management to the next level.

Want to learn how can support your data integration and management requirements in real-time and with concrete benefits? See for yourself with a free, 14-day trial. If yours is a unique use case, you can schedule an intro call for a demo of the platform. One of our talented Solutions Engineers will show you, one-on-one, how our platform can help you optimize your data workflows and achieve your data management goals.


What does an ETL developer do?

An ETL developer designs, develops, and maintains ETL processes that extract data from various sources, transform it into a suitable format, and load it into a data warehouse or other storage systems.

How does a data engineer's role differ from an ETL developer?

While ETL developers focus on the ETL process, data engineers build and maintain the overall data infrastructure, including data pipelines that facilitate the smooth flow of data from source systems to data storage and processing platforms.

What skills are needed to become an ETL developer?

ETL developers need strong SQL skills, proficiency with ETL tools (such as Informatica, Talend, and SSIS), and an understanding of data warehousing concepts. They also require problem-solving, attention to detail, and analytical thinking skills.

What are the key responsibilities of a data engineer?

Data engineers design, build, and maintain data pipelines, ensure data collection from various sources, implement data processing frameworks, and collaborate with data scientists and analysts to meet data needs.

What programming languages should a data engineer know?

Data engineers should be proficient in programming languages such as Python, Java, and Scala.

How do ETL developers contribute to data quality?

ETL developers enhance data quality by implementing robust ETL processes that cleanse, validate, and enrich data, ensuring it is reliable and consistent.

What career opportunities are available for data engineers?

Data engineers can advance to roles such as lead data engineer, data engineering manager, or specialize in areas like big data engineering or cloud data engineering.

Can an ETL developer transition to a data engineer role?

Yes, an ETL developer can transition to a data engineer role by acquiring additional skills in data engineering tools and technologies, such as programming languages and big data frameworks.

What is the future outlook for ETL developers and data engineers?

The demand for both ETL developers and data engineers is expected to grow as organizations continue to rely on data-driven decision-making and the need for robust data management solutions increases.