Master data engineering to build modern, scalable data infrastructures.

From BigQuery to Dataflow, our training covers the entire data lifecycle. Develop key skills to create robust pipelines, optimize queries and ensure data quality.

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Why Train in Data Engineering in 2026?

In 2026, Data Engineering has become the essential pillar of any data-driven strategy. Data is the new oil, but without robust pipelines and scalable architectures, it remains untapped. Training in Data Engineering enables you to build the solid foundations necessary for AI, Machine Learning, and advanced analytics. Mastering modern tools (BigQuery, Snowflake, dbt, Airflow) and data engineering best practices has become essential to ensure quality, governance, and performance of large-scale data systems.

Robust Pipelines

Build reliable and scalable data architectures to process massive volumes.

Data Quality

Ensure data quality and governance for reliable insights.

Optimal Performance

Optimize queries and storage to reduce costs and improve speed.

Simplified Integration

Connect all your data sources with modern and efficient ETL tools.

Who is this for?

Data Engineering concerns all professionals involved in collecting, processing, and leveraging data. From engineers who build pipelines to analysts who use them, to architects who design data infrastructure.

Decision-Maker & Governance Roles

Chief Data OfficerData ArchitectHead of DataData Governance Manager

Engineering & Implementation Roles

Data EngineerAnalytics EngineerETL DeveloperPlatform Engineer

Analysis & Operations Roles

Data AnalystBusiness Intelligence DeveloperData ScientistML Engineer

Browse our Data Engineering training by theme

Data Engineering

Formations Data Engineering : Pipelines, ETL, BigQuery, Dataflow, Data Lakes.

View training Data Engineering

Data Analytics

Formations Data Analytics : BI, visualisation, analyses.

View training Data Analytics

Popular Data Engineering Training

SFEIR Institute best-sellers in Data.

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Introduction to Data Analytics on Google Cloud

This course is an introduction to data analytics on Google Cloud. It is designed for learners who have no prior experience with data analytics or Google Cloud. The course covers the basics of data analysis, including collection, storage, exploration, visualization, and sharing. It also introduces learners to Google Cloud's data analytics tools and services. Through video lectures, demos, quizzes, and hands-on labs, the course demonstrates how to go from raw data to impactful visualizations and dashboards.

1 d

Fundamental

Introduction to Data Engineering on Google Cloud

In this course, you learn about data engineering on Google Cloud, the roles and responsibilities of data engineers, and how those map to offerings provided by Google Cloud. You also learn about ways to address data engineering challenges.

1 d

Fundamental

Analyzing and Visualizing Data in Looker

In this course, you learn how to do the kind of data exploration and analysis in Looker that would formerly be done primarily by SQL developers or analysts. You learn to leverage Looker's modern analytics platform to find and explore relevant content in your organization's Looker instance. You also discover how to ask questions of your data, create new metrics as needed, and build and share visualizations and dashboards to facilitate data-driven decision-making.

1 d

Fundamental

BigQuery for Data Analysts

This course is designed for data analysts who want to learn about using BigQuery for their data analysis needs. Through a combination of videos, labs, and demos, we cover various topics that discuss how to ingest, transform, and query your data in BigQuery to derive insights that can help in business decision-making.

2 d

Intermediate

Data Integration with Cloud Data Fusion

This 2-day course introduces learners to Google Cloud's data integration capability using Cloud Data Fusion. In this course, we discuss challenges with data integration and the need for a data integration platform (middleware). We then discuss how Cloud Data Fusion can help to effectively integrate data from a variety of sources and formats and generate insights. We take a look at Cloud Data Fusion's main components and how they work, how to process batch data and real time streaming data with visual pipeline design, rich tracking of metadata and data lineage, and how to deploy data pipelines on various execution engines.

2 d

Intermediate

Orchestrate BigQuery Workloads with Dataform

Dataform is a service for data analysts to develop, test, version control, and schedule complex SQL workflows for data transformation in BigQuery. In this course you will explore the components of Dataform core, learn how to define tables and dependencies in SQLX, document BigQuery tables and views, understand BigQuery security settings and how to manage these with Dataform, write assertions, execute SQL workflows, and explore additional advanced use cases.

1 d

Intermediate

Need a custom Data Engineering training program?

We adapt our Data Engineering training to your specific needs: customized content, business use cases, flexible scheduling, and dedicated support.

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