What Does a Data Engineer Do?

A Data Engineer builds and maintains the systems and pipelines that collect, store and prepare data for analysis, reporting and AI.

Every time a business needs to combine customer information, update a reporting dashboard or supply data to a machine learning model, that information has to move between systems accurately and consistently. Data Engineers make that possible.

Their work focuses on the infrastructure behind data rather than the analysis itself. They write code, build pipelines, manage databases and solve the technical problems that can interrupt the flow of information.

It’s a role that combines software engineering, database expertise and an understanding of how different teams use data.

If you’re considering a career in Data Engineering, understanding what the job involves day to day is a good place to start. This guide covers the responsibilities, technical skills and how the role compares with Data Science and Analytics Engineering, so you can decide whether it’s the right direction for you.

Data Engineer core responsibilities

Data Engineers manage the systems that move, store and prepare data for use across a business. Their main responsibilities include:

Designing and building data pipelines

Developing automated ETL (extract, transform, load) and ELT (extract, load, transform) pipelines that move data between systems, including managing schedules, dependencies and errors.

Integrating data from different sources

Connecting information from applications, APIs, websites and other platforms, and handling differences in data formats and structures.

Managing databases and data warehouses

Designing, maintaining and optimising databases, cloud data warehouses and data lakes to support efficient storage and access.

Maintaining data quality and reliability

Monitoring pipelines, checking for missing or incorrect records and resolving failures before they affect reporting or analysis.

Structuring data for other teams

Cleaning, organising and modelling data so Data Scientists, analysts and other teams can use it for reporting, analytics and machine learning.

Working with technical and business teams

Collaborating with Data Scientists, analysts, Software Engineers and stakeholders to understand data requirements and develop the systems to meet them.

Improving performance and infrastructure

Optimising processing speed, storage costs and system performance as data volumes grow, including managing distributed systems in larger organisations.

A day in the life of a Data Engineer

A Data Engineer’s day often starts by checking what happened while they were away.

Many pipelines run automatically overnight, collecting information from operational systems and updating databases or reporting platforms. If a job failed, records are missing or a source system changed its format, the first priority may be finding the cause and fixing it before other teams start using the data.

Once existing systems are running properly, the focus shifts to development. That could mean writing Python or SQL to build a new pipeline, connecting an API, improving a database query or testing a change before deployment.

They also spend time reviewing code and working with colleagues. A Data Scientist might need a new dataset for a model. An analyst might report that dashboard figures no longer match the source system. Both can lead to Data Engineering work.

Not every day involves troubleshooting. Engineers also improve monitoring, automate repetitive processes and make pipelines faster or cheaper to operate.

The balance changes between organisations, but keeping existing systems running is as much a part of the job as building new ones.

Data Engineer vs Data Scientist: what’s the difference?

Data Engineers and Data Scientists work with data, but their responsibilities differ.

A Data Engineer focuses on the systems that collect, process, store and deliver data. Their job is to make sure information reaches the people and applications that need it in a usable form.

A Data Scientist uses data to investigate questions, identify patterns, develop statistical models and make predictions.

For example, a Data Engineer might build the pipeline that brings customer transactions into a central data platform. A Data Scientist could then use those transactions to develop a model predicting customer behaviour.

The two roles work closely together. Data Scientists may also prepare and transform data themselves, while Data Engineers need to understand how their systems support analysis and modelling.

For a fuller explanation of the different responsibilities, read What Does a Data Scientist Do?.

Data Engineer vs Analytics Engineer: what’s the difference?

The distinction between Data Engineers and Analytics Engineers largely comes down to which parts of the data platform they manage and who uses their work.

Data Engineers typically focus on the underlying infrastructure. They build ingestion pipelines, connect source systems, manage storage and orchestration, and make sure data moves consistently into warehouses, lakes and other platforms.

Analytics Engineers work closer to the reporting and analysis layer. They take data already available in a warehouse and transform it into consistent, documented datasets that analysts and business teams can use.

For example, a Data Engineer might build the pipeline that collects sales transactions from several applications and loads them into a cloud warehouse.

An Analytics Engineer could then use SQL and a tool such as dbt to create a standardised revenue model, apply business definitions, test the data and document it for reporting.

Both roles write code, transform data and care about quality. The distinction usually comes down to which part of the data platform each role manages.

In larger data teams, the division may be clear. In smaller businesses, one person may handle ingestion, transformation, modelling and delivery. Job titles alone do not always tell you which responsibilities a role includes.

Data Engineers generally manage data infrastructure and movement, while Analytics Engineers focus on preparing warehouse data for consistent business use.

For a clearer picture of the role and responsibilities, read What Does an Analytics Engineer Do?

Key skills a Data Engineer needs

Data Engineering combines programming, databases and infrastructure management. The most useful skills depend on the systems an organisation uses, but several form the foundation of the role.

• SQL and programming: SQL is essential for querying and transforming data. Python is widely used to automate processes and develop pipelines, while Scala and Java are also used in larger processing environments.
• ETL and ELT pipeline design: Data Engineers need to understand how information moves from source systems into storage and how to manage dependencies, scheduling and failures.
• Databases and data warehouses: Experience with relational databases, data modelling and warehouse platforms helps engineers organise information and improve query performance.
• Cloud platforms: AWS, Microsoft Azure and Google Cloud provide services for data storage, processing and infrastructure management.
• Big data and orchestration tools: Technologies such as Spark, Kafka, Hadoop and Airflow support distributed processing, streaming data and automated workflows.

Problem-solving and communication matter too. Data Engineers need to investigate technical failures, understand other teams’ requirements and explain how changes to data systems affect their work.

For a closer look at developing these skills, qualifications and certifications, read How to Become a Data Engineer.

Which industries employ Data Engineers?

Data Engineers work across industries that collect and use large volumes of information.

In financial services, they build systems that support transaction processing, risk analysis and regulatory reporting. Technology and SaaS businesses use Data Engineering to connect product data, customer activity and operational systems.

Retail and e-commerce companies rely on pipelines for sales reporting, inventory management and customer analytics. In healthcare, Data Engineers help integrate clinical, operational and research data, while telecommunications companies use large-scale data systems to manage network information and customer activity.

The role is not limited to these sectors. Organisations need Data Engineering expertise whenever their data comes from multiple sources, requires substantial processing or must remain consistently available to other systems.

Summary and key takeaways

• Data Engineers build and maintain data infrastructure. Their responsibilities include pipelines, databases, data quality, storage and system performance.
• The role combines development and maintenance. A typical day can involve writing code, fixing pipeline failures, reviewing data quality and improving existing systems.
• Data Engineers and Analytics Engineers share overlapping skills but focus on different areas. Data Engineers typically manage ingestion and infrastructure, while Analytics Engineers prepare trusted datasets for business analysis.
• Explore How to Become a Data Engineer for career routes and skills, or How Much Does a Data Engineer Make? for salary benchmarks and progression.

Frequently asked questions

What is the difference between a Data Engineer and a Data Scientist?

A Data Engineer builds the pipelines, databases and infrastructure that collect, process and deliver data. A Data Scientist uses data to investigate questions, develop models and generate insights. They often work together, with Data Engineers maintaining the systems that make data available for analysis and machine learning.

What is the difference between a Data Engineer and an Analytics Engineer?

Data Engineers typically focus on ingestion, storage, pipeline infrastructure and the movement of data between systems. Analytics Engineers usually work with data already in a warehouse, transforming it into tested, documented datasets for reporting and analysis. The responsibilities overlap, especially in smaller teams where one person may do both.

Do Data Engineers write code every day?

Many Data Engineers write or review code regularly, particularly SQL and Python. Their work includes developing pipelines, transforming data, automating processes and fixing technical issues. However, some days involve more monitoring, troubleshooting, infrastructure planning or collaboration, depending on the organisation and the engineer’s level of responsibility.

What industries hire Data Engineers?

Data Engineers work in financial services, technology and SaaS, retail and e-commerce, healthcare and telecommunications. These industries use data pipelines for activities such as transaction processing, customer analytics, operational reporting and large-scale data management. Organisations in other sectors also hire Data Engineers when they need systems to collect, process and store information.