Structured data on the course pages lists an online full-time Data Engineering cohort Oct 26 to Nov 27, 2026 (5,900 EUR) and online and campus Data Analytics cohorts from Oct 17, 2026 and Jan 18, 2027 (7,400 to 7,900 EUR); no US campus exists. source
A European bootcamp brand with a short, advanced data engineering course on the GCP, Airflow, dbt and Spark stack and a longer analytics bootcamp, sold in euros per country. It suits people in or near European time zones who already have a technical background and want an intense in-person or live-online format. Before paying, confirm the cohort time zone and the price in your country, and ask for the ISA terms in writing if you use one.
The facts
| Category | Data engineering and analytics, AI and machine learning, Software engineering |
|---|---|
| Website | lewagon.com/data-engineering-course |
| Based | Paris, France · founded 2013 |
| Format | online-live, in-person |
| Programs | Data Engineering bootcamp (5 weeks full-time, online or campus; advanced entry), Data Analytics bootcamp (9 weeks full-time or about 6 months part-time), Data Science & AI bootcamp, AI Software bootcamp |
| Length | 5 weeks (Data Engineering online cohort runs 2026-10-26 to 2026-11-27 (5 weeks) after 40 hours of prep work, per the page's JSON-LD; Data Analytics runs 9 weeks full-time (2026-10-19 to 2026-12-18) or about 6 months part-time.) verified |
| Tuition | No USD price. JSON-LD offers on the course pages: Data Engineering online 5,900 EUR; Data Analytics online 7,400 EUR; Data Analytics Paris or Berlin campus 7,900 EUR. The visible page sends you to a per-country financing form and the country list does not include the United States. unknown |
| Financing | upfront, installments, loan, isa, scholarship, employer London campus page lists installment plans, partner loans, scholarships for underrepresented groups, an Income Share Agreement described as 'study now and repay only once you land a job' with no percentage or cap published, and employer-funded training. Availability varies by country. |
| Admission | basic assessment Application plus an admissions call. The Data Engineering course is 'designed for students with professional experience in data, or a technical degree' and requires 40 hours of prep work in Linux, Git, Python and SQL. |
| Stack | Python, SQL, Docker, GitHub, Google Cloud, BigQuery, PostgreSQL, Airflow, dbt, Apache Spark, Kubernetes, Pub/Sub, Dataflow, Terraform |
Outcomes
This program does not publish job-placement or salary outcomes. Treat any number you hear from admissions as unverifiable.
Interview-prep depth
2 / 5
Career services blurb on the course page mentions technical interview training and 1:1 coaching; the syllabus modules themselves are all engineering content.
Job support: structured. Site promises lifetime access to career advisors, 'technical interview training', personal branding coaching and 1:1 coaching, plus a network it describes as 1,000+ hiring partners. No hours or placement figures are published.
Whatever the program covers, the interview itself is free to prepare for here: coding patterns, system design, practice prompts, company guides.
What a careful reader should know
Le Wagon has no US campus; online cohorts are attached to a campus time zone (the listed Data Engineering cohort is 'Le Wagon Online, timezone Europe'). Prices are set per country and are not shown on the English course pages. The company is Qualiopi-certified in France, which matters for French public funding, not for US students. Course Report's Data Engineering listings carry 5.0 ratings from only 4 and 1 reviews.
Third-party signals: Course Report 4.95 (3810 reviews). Graduation-time star ratings are collected by the schools; weigh them accordingly.
Sources (5)
This directory takes no lead fees or placement payments from any program. Where a program runs an affiliate scheme we say so on its page and mark the link. Facts carry a badge: verified means we read it on a primary source on the date shown, reported means a third party said it, unknown means nobody we trust has published it.
Useful next steps:
