Data Engineer - Gaming Data Mesh & Analytics

Gaming1; Luik; On-site; EUR 65,000 - 90,000; Full time

Job summary

Gaming1 is seeking a Data Engineer to build reliable, scalable data products that support decision-making across the company. You will design, build and operate data pipelines and models in SQL and PySpark, and develop analytics-ready tables for analysts and Power BI reports.

You will own data quality, tune jobs, evolve schemas, and contribute to a data-mesh culture where domains manage their data with shared platforms and engineering practices.

Qualifications

• Strong SQL skills and the ability to write and optimise complex queries.
• Experience with PySpark (or Python) in a data-engineering context.
• Solid data-modelling knowledge, including Kimball-style star schemas.
• Around 3–6 years of hands‑on data‑engineering experience, with the autonomy to own work from source to reporting‑ready data.
• A collaborative mindset, clear communication skills and comfort working in the gaming industry.
• Professional English.

Responsibilities

• Build data products: design and develop pipelines and models in SQL and PySpark, from ingestion to analytics-ready tables.
• Model data: create clear dimensional models that analysts and Power BI reports can use directly.
• Own quality: implement tests based on business rules, investigate failed loads and improve reliability.
• Improve what exists: tune jobs, evolve schemas and pipelines, reduce compute costs, and leave the codebase better than you found it.
• Share knowledge: document your work in the project’s Git repository so it is easy to understand, maintain and reuse.

Skills

SQL

PySpark

Data modelling

Experience 3-6y

Collaboration

English

Tools

Power BI

Airflow

Job description

Gaming1 is seeking a Data Engineer to build reliable, scalable data products that support decision-making across the company. You will design, build and operate data pipelines and models in SQL and PySpark, and develop analytics-ready tables for analysts and Power BI reports.

You will own data quality, tune jobs, evolve schemas, and contribute to a data-mesh culture where domains manage their data with shared platforms and engineering practices.