Data Engineer
This mid-level Data Engineer role at Inetum focuses on designing and maintaining scalable data pipelines with Apache Airflow, ensuring seamless data flow between sources and analytical systems. The position emphasizes collaboration with analysts and stakeholders to deliver reliable, accessible data solutions while optimizing infrastructure performance.
Skills required
About this role
We are looking for a Data Engineer to be responsible for building and maintaining the infrastructure that supports the organization’s data architecture.
The role involves creating and managing data pipelines using Airflow for data extraction, processing, and loading, ensuring their maintenance, monitoring, and stability.
The engineer will work closely with data analysts and end-users to provide accessible and reliable data
Main Tasks and Responsibilities:
- Responsible for maintaining the infrastructure that supports the current data architecture
- Responsible for creating data pipelines in Airflow for data extracting, processing, and loading
- Responsible for data pipelines maintenance, monitoring, and stability
- Responsible for providing data access to data analysts and end-users
- Responsible for DevOps infrastructure
- Responsible for deploying Airflow dags to production environment using DevOps tools
- Responsible for code and query optimization
- Responsible for code review
- Responsible for improving the current data architecture and DevOps processes
- Responsible for delivering data in useful and appealing ways to users
- Responsible for performing and documenting analysis, review, and study on specified regulatory topics
- Responsible for understanding business change and requirement needs, assess the impact and the cost
Qualifications
Technical skills:
- Advanced Python(Mandatory)
- Experience in creating APIs in Python - At least Flask (Mandatory)
- Experience in documenting and testing in Python (Mandatory)
- Advanced SQL skills and relational database management (Oracle is Mandatory, SQL server is desirable, PostgreSQL is desirable )
- Experience with Data Warehouses
- Hadoop ecosystem - HDFS + Yarn (Mandatory)
- Spark Environment Architecture (Mandatory)
- Advanced PySpark - (Mandatory)
- Experience in creating and maintaining distributed environments using Hadoop and Spark
- Data Lakes - Experience in organizing and maintaining data lakes (Mandatory) - S3 is preferred
- Experience with Parquet file format is mandatory, Avro is a plus
- Apache Airflow - Experience in both pipeline development and deploying Airflow in distributed environment (Mandatory)
- Containerization - Docker is Mandatory
- Kubernetes (Mandatory)
- Apache Kafka (Mandatory)
- Experience in automating applications deployment using DevOps tools - Jenkins is Mandatory, Ansible is a plus
- Agile methodologies (At least SCRUM)
Language Skills:
- Fluent in English (Mandatory)
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