Inetum is a global leader in IT services, dedicated to providing innovative solutions to our clients. We are committed to fostering a dynamic, inclusive workplace that values diversity, where creativity and collaboration thrive. We operate in 19 countries with more than 28,000 employees worldwide.

If you are looking for a dynamic, innovative, and technology-driven company, Inetum is the place for you! Come be Inetum!

The Data Engineer will be responsible for designing, developing, and maintaining scalable and reliable data pipelines for a financial services project. The role focuses on backend data processing, data quality, and integration of multiple data sources in a cloud-based environment, working closely with international teams.

Key Responsibilities

• Design, develop, and maintain end-to-end ETL/ELT data pipelines to process large volumes of structured and semi-structured data.
• Implement backend data solutions using Python and SQL, applying Object-Oriented Programming (OOP) to ensure modularity, reusability, and maintainability.
• Orchestrate data workflows using Apache Airflow, including scheduling, monitoring, and failure handling.
• Process and transform large datasets using PySpark in distributed environments.
• Integrate data from multiple sources, including APIs, relational databases, and cloud storage systems.
• Manage and utilize AWS S3 for data storage and data lake architectures.
• Apply data quality checks, validation rules, and deduplication logic to ensure data consistency and accuracy.
• Develop, maintain, and support CI/CD pipelines using Bitbucket, ensuring controlled deployments, versioning, and code quality.
• Collaborate with cross-functional and international teams, contributing to technical discussions and documentation in English.
• Support downstream data consumers by ensuring datasets are well-structured, documented, and ready for analytics or reporting.
• Troubleshoot and resolve data pipeline issues, performance bottlenecks, and data inconsistencies.

• Programming Languages: Python, SQL
• Programming Paradigms: Object-Oriented Programming (OOP)
• Data Processing: PySpark
• Orchestration: Apache Airflow
• CI/CD: Bitbucket
• Cloud & Storage: AWS (S3)
• Data Sources: APIs, relational databases, parquet files
• Data Architecture: ETL/ELT pipelines, data lakes

Required Skills & Experience

• Strong experience in data engineering and backend data development.
• Solid knowledge of Python and SQL, with practical application of OOP principles.
• Experience building and maintaining production-grade ETL/ELT pipelines.
• Hands-on experience with Apache Airflow for workflow orchestration.
• Experience with CI/CD practices
• Experience working with distributed data processing frameworks such as Spark / PySpark.
• Familiarity with cloud-based data platforms, preferably AWS.
• Ability to work autonomously while collaborating with remote, international teams.
• Professional working proficiency in English.

Nice to Have

• Experience in financial services or regulated environments.
• Familiarity with data quality frameworks, monitoring, or observability tools.
• Exposure to Oracle Apex.
• Experience working in agile and/or DevOps-oriented teams.

The candidate is expected to work in a Hybrid model, 50/50 frame work.