What is a data engineer ?
A data engineer is a technology professional who specializes in data engineering, the discipline of designing, building, and maintaining the data systems that power modern organizations. Their main responsibility is to develop and optimize data pipelines that move information from multiple sources into databases, warehouses, or cloud platforms, ensuring it is clean, reliable, and accessible. Data engineers also manage data processing at scale, handling both real-time and batch workflows to support analytics, business intelligence, and machine learning applications. Unlike data scientists, who focus on analyzing and modeling, data engineers create the infrastructure that makes advanced analysis possible. By combining programming skills, database expertise, and knowledge of cloud computing, they enable companies to transform raw information into actionable insights. In today’s data-driven economy, the role of a data engineer is essential for organizations that need to make informed, evidence-based decisions and build scalable digital solutions.
A key role in big data
The explosion of big data is profoundly transforming businesses, increasing the demand for data engineers. This growth stems from the need to manage exponentially growing data from various sources, such as:
- Social networks
- Connected devices
- Online transactions
As a result, companies are investing heavily in data infrastructure to stay competitive and leverage their data effectively. In this context, the data engineer plays a pivotal role in the digital transformation of organizations.
This professional contributes to the company’s data strategy and enhances business processes by optimizing data utilization. Their technical expertise addresses challenges related to performance, scalability, and data security.
Differences between data engineer, data analyst, and data scientist
Understanding the distinction between these three data-related roles is essential to grasp their respective contributions to the data value chain.
The data engineer focuses on architecture and infrastructure, laying the groundwork for other data professionals. They ensure data availability, quality, and performance for data analysts (who conduct descriptive analyses) and data scientists (who build predictive models). A construction analogy illustrates their complementarity:
- The data analyst arranges and decorates the rooms (analysis and visualization).
- The data scientist installs smart systems (predictive models and AI).
What are the responsibilities of a data engineer ?
A data engineer is responsible for designing and maintaining data warehouses and data lakes, developing robust pipelines for data collection and processing, and implementing scalable data storage solutions. Their key responsibilities include:
- Designing and implementing scalable data architectures
- Developing and maintaining ETL (Extract, Transform, Load) pipelines
- Optimizing database performance
- Implementing data security and governance protocols
- Automating data collection and processing workflows
- Creating detailed technical documentation
- Collaborating with business teams to understand their needs
- Staying up to date with technological advancements and evaluating new solutions
A data engineer also ensures the deployment of data infrastructure, requiring close collaboration with DevOps and security teams. They contribute to defining standards and best practices for data management.
What do you need to become a data engineer?
Technical Skills
- Proficient in programming languages: Python, Java, Scala, SQL
- Expertise in big data technologies: Apache Spark, Hadoop, Kafka
- Strong data architecture & modeling skills for relational & NoSQL databases
- Experience with cloud platforms: AWS, Google Cloud, Microsoft Azure
- Knowledge of ETL/ELT tools: Talend, Informatica, Fivetran
Analytical Skills
- Ability to analyze complex datasets
- Optimize data pipelines and workflows
- Ensure data quality and reliability
Communication & Collaboration
- Work with data scientists, analysts, and business stakeholders
- Document processes and explain technical concepts clearly
Advanced & Optional Skills
- Basic machine learning and AI integration
- Understanding of data governance, security, and compliance
What tools do data engineers use?
Data engineers rely on a variety of tools to design, build, and maintain data pipelines, process large datasets, and manage complex data systems. Key tools and technologies include:
Workflow Orchestration
- Apache Airflow – automates, schedules, and monitors data workflows.
- Luigi – handles batch data pipelines and task dependencies.
Big Data Processing
- Apache Spark – distributed data processing for large-scale analytics.
- Hadoop – framework for storing and processing massive datasets.
Cloud Computing Platforms
- AWS (Amazon Web Services) – services like S3, Redshift, EMR.
- Google Cloud Platform (GCP) – BigQuery, Dataflow, Cloud Storage.
- Microsoft Azure – Azure Data Factory, Synapse Analytics.
Data Warehousing
- Snowflake – cloud-native data warehouse.
- Redshift – scalable data storage and querying.
- BigQuery – serverless analytics platform.
Data Pipeline & ETL Tools
- Talend – ETL (Extract, Transform, Load) workflows.
- Informatica – enterprise data integration.
- Fivetran – automated data connectors for pipelines.
Other essential skills: SQL, Python, Scala, version control (Git), and monitoring tools for ensuring pipeline reliability.
Training to become a data engineer
The Bachelor of Science in Data Science for Responsible Business at emlyon business school provides a comprehensive and recognized education for aspiring data engineers. This program stands out for:
- In-depth technical training covering all required competencies
- Hands-on projects in real-world conditions with partner companies
- A faculty of industry experts
- A strong international dimension
- An active alumni network in the data sector
- Partnerships with leading companies in the industry
To further develop specialized expertise and stay updated in this rapidly evolving field, professionals can pursue various industry certifications, particularly from major cloud providers.
What is the Salary of a Data Engineer?
The salary for data engineer jobs varies by experience, location, and company size, but overall it is a well-paid and in-demand profession.
Average Salary Range (France, 2025)
- Entry-level : €34,000 – €47,000
- Junior : €40,000 – €56,000
- Mid-level : €44,000 – €61,000
- Senior: €50,000 – €70,000+
Key Insights
- Entry-level data engineers start around €34,000–€47,000 annually.
- The average salary for data engineers is about €50,000 per year.
- Experienced professionals can reach €70,000+, especially in Paris and large tech companies.
- Bonuses and performance-based incentives often increase total compensation.
Global Salary Comparison for Data Engineers (2025)
United States
- Average total (Glassdoor): ~$133,600/year, including bonuses and profit sharing Coursera
- Senior data engineers: ~$194,300/year (Coursera)
- Experience-based estimates (LinkedIn data):
- Entry-level: $80K–$120K
- Mid-level: $120K–$160K
- Senior: $160K–$200K+
- Lead/Principal: $200K–$250K+ (LinkedIn)
- Location-based ranges:
- San Francisco: $160K–$220K
- New York: $150K–$210K
- Seattle: $140K–$200K
- Austin: $130K–$180K (LinkedIn)
- Corporate benchmarks (e.g., AT&T via H-1B data):
- Principal Data/AI Engineers: up to $197K/year (Business Insider)
- Reddit-sourced real-world examples:
United Kingdom
- Indeed: ~£55,231/year (average), with London at £64,249 (Indeed)
- Glassdoor: Median ~£52,708; range typically from £40,116 to £70,665; top earners at ~£94,355 (Glassdoor)
- Jobted: Average base salary ~£49,100/year; starting at ~£25,300; top roles >£110,000 (uk.jobted.com)
- Jobted by experience:
- Entry-level (~3 years): ~£33,700
- Mid-career (4–9 years): ~£48,400
- Senior (10–20 years): ~£72,200
- Late career (20+ years): ~£84,500 (uk.jobted.com)
- Robert Walters: London salaries range £60K–£135K; North: £50K–£120K (robertwalters.co.uk)
- Bristow Holland: National average ~£55K–£70K; entry-level ~£41K; experienced >£100K; London ~£75K–£100K (Bristow Holland)
- Reddit example: Remote UK-based mid-level DE earning £72K (Reddit)
Germany
- Terratern: Average ~€72,500 (30% above national IT average); senior engineers at Bosch earn €103K+; remote roles pay ~18% more (Terratern)
- Glassdoor: Median total pay ~€70K; typical range €59,875–€82,000; senior/lead reach €138K–€224K (Glassdoor)
- Reddit (Robert Half):
- Junior: €43K–€60K
- Mid-level: €65K–€90K
- Senior (8+ years): €80K–€130K+ (Reddit)
What are the career paths for data engineers?
A career as a data engineer offers strong growth opportunities across technical and leadership roles. Depending on experience and skills, professionals can follow multiple paths:
Entry-Level Roles
- Junior Data Engineer – supports building and maintaining data pipelines, learning core technologies like SQL, Python, and cloud platforms.
- Focus on data integration, cleaning, and processing.
Mid-Level Roles
- Data Engineer / Professional Data Engineer – independently designs, implements, and optimizes data workflows.
- Gains expertise in big data tools, ETL processes, and data warehouse management.
Senior Roles
- Senior Data Engineer – leads complex projects, mentors junior staff, and ensures data architecture and pipeline efficiency.
- Often collaborates with data architects and data scientists to meet business requirements.
Leadership & Specialized Roles
- Data Architect – designs scalable data infrastructure and oversees data management strategy.
- Analytics Engineer / Data Platform Lead – focuses on pipeline reliability, performance, and strategic implementation.
Key Insights
- Growth is rapid in tech, finance, healthcare, and cloud services.
- Strong programming, cloud computing, and big data expertise accelerate promotions.
Conclusion
The data engineer role is essential for any company looking to leverage its data assets. By designing and maintaining the infrastructure needed to manage massive datasets, this expert enables organizations to transform raw data into actionable insights, driving decision-making and operational success.
Data Engineer FAQ
Data engineering is one of the fastest-growing fields in technology. Below, you’ll find answers to the most common questions about data engineers, salaries, skills, roles, and career paths
Yes. Data engineers earn high salaries because their expertise is in high demand. Entry-level professionals often start above the average tech salary, while senior data engineers and lead roles can exceed $120,000–$150,000 per year in the U.S. In Europe, salaries are also competitive, especially in tech hubs like London, Berlin, and Paris. Compensation grows with experience in cloud platforms, data pipelines, and big data systems.
Data engineering is the discipline of building, maintaining, and optimizing data systems and pipelines. Its importance lies in enabling businesses to:
- Collect and organize massive volumes of data.
- Ensure data is reliable, clean, and accessible.
- Power analytics, machine learning, and AI applications.
Without strong data engineering, companies cannot make data-driven decisions or scale their digital strategies effectively.
Yes. Coding is essential in data engineering. A professional data engineer must master:
- SQL – for querying and managing databases.
- Python – for automation, data pipelines, and processing.
- Java or Scala – often used in big data frameworks like Apache Spark.
Coding allows engineers to design efficient systems and solve complex data challenges.
Python is critical, but not sufficient alone. Employers usually expect additional skills:
- SQL for database management.
- Apache Spark, Hadoop, or Kafka for big data processing.
- Cloud platforms (AWS, Google Cloud, Azure) for scalable infrastructures.
- ETL/ELT tools like Talend or Fivetran.
Python provides a strong foundation, but multi-tool expertise is key for success.
Yes. Data engineering is one of the most in-demand tech careers worldwide. The rise of big data, cloud computing, and AI has created a talent shortage, making skilled engineers highly sought-after across industries. Companies in tech, finance, healthcare, e-commerce, and government are all hiring.
There are many benefits to pursuing a career in data engineering:
- High salaries and excellent job security.
- Work on cutting-edge technologies in big data and AI.
- Opportunities for advancement into senior or architect roles.
- Contribution to business innovation and data-driven transformation.
It’s a rewarding career for professionals passionate about technology, problem-solving, and data systems.
To become a data engineer, you typically need:
- A degree in computer science, engineering, or mathematics (not always required but helpful).
- Skills in programming, SQL, and database management.
- Knowledge of data modeling, cloud computing, and big data frameworks.
- Strong problem-solving and communication skills.
Professional certifications in AWS, Google Cloud, or Microsoft Azure can also boost career prospects.
A data engineer is responsible for:
- Designing and maintaining data pipelines.
- Managing databases and warehouses.
- Ensuring data quality, security, and accessibility.
- Collaborating with data scientists and analysts to provide usable datasets.
In short, they transform raw data into a reliable resource for decision-making.
These fields are related but distinct:
- Data engineering – builds the infrastructure and pipelines.
- Data science – applies models and algorithms to extract insights.
- Data analysis – interprets and visualizes data for business decisions.
Data engineering is the foundation that enables the other two fields to operate effectively.
Data engineers work across many industries, including:
- Technology & software companies
- Finance and banking
- Healthcare and biotech
- E-commerce and retail
- Government and public sector
Any organization that relies on data systems, analytics, or AI requires skilled data engineers.