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# Senior Data Engineer (Cloud Data Platform)

- **Company:** Taleo Consulting
- **Location:** Brussel, Belgium
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Microsoft Azure, BigQuery, Code Review, Information Systems, Continuous Integration, Information Engineering, Data Infrastructure, Extract Transform Load (ETL), Identity and Access Management, Python (Programming Language), Meta-Data Management, Standard Sql, DataOps, Privacy Controls, Cloud Platform System, Data Ingestion, Azure Data Factory, Snowflake, Apache Spark, Build Management, Pyspark, Information Technology, Apache Kafka, Data Management, Software Version Control, Data Pipelines, Databricks
- **Published:** October 22, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=0cf780bbe47c41b4

## About the Role

* Master's or Bachelor's degree in computer science, engineering or information systems.
 * 5+ years in data engineering, including significant experience on Azure, AWS or GCP.
 * Strong SQL and Python; Spark / PySpark and a lakehouse platform (Databricks, Snowflake, Fabric or BigQuery).
 * Orchestration and transformation tools (Airflow, Azure Data Factory, dbt); Kafka is a plus.
 * Solid knowledge of data modelling, data quality and metadata management; GDPR awareness.
 * Fluent in English, with Dutch or French.

Soft skills:

 * Team player
 * Quick self-starter with a proactive attitude
 * Strong problem-solving and analytical skills
 * Clear communication across business and technology teams
 * Comfortable in a dynamic, multicultural environment

## Description

Our client's Data Platform & Engineering team builds the data foundation for analytics and AI across four streams:

 * Data Ingestion & Integration
 * Lakehouse Modelling & Data Products
 * Data Quality & Observability
 * Security, Governance & DataOps

Your role:

 * Design and build scalable batch and streaming pipelines (ELT/ETL) on a modern cloud lakehouse platform.
 * Develop data models and data products across raw, curated and consumption layers, following domain-ownership and data mesh principles.
 * Implement automated data quality checks, testing, lineage and observability for critical pipelines.
 * Define and monitor SLAs / SLOs, manage incidents and perform root-cause analysis on data issues.
 * Apply security and privacy controls (access management, masking, encryption, retention) in line with GDPR and internal policies.
 * Publish metadata, lineage and business definitions to the data catalogue together with data stewards.
 * Champion DataOps practices (version control, code review, CI/CD, infrastructure-as-code) and mentor junior engineers.