We are seeking a talented and experienced Data Engineer to join our team. As a Data Engineer, you will play a critical role in designing, building, and maintaining the data infrastructure required for processing and analyzing large datasets. You will collaborate with data scientists, analysts, and other engineers to ensure efficient data flow, storage, and access across the organization. The ideal candidate will have a passion for data and an eye for building scalable data solutions.
Responsibilities
- Data Pipeline Development: Design, implement, and maintain scalable, efficient, and robust data pipelines that enable the extraction, transformation, and loading (ETL) of data from various sources.
- Data Modeling: Work closely with data architects and analysts to design and maintain optimized, high-quality data models that support business intelligence and reporting needs.
- Data Warehouse Management: Manage and optimize the performance of data warehouses (e.g., Google BigQuery, Snowflake, Redshift, etc.) to ensure the reliable availability of data.
- Data Integration: Develop solutions to integrate data from various systems and data sources, ensuring accuracy, consistency, and quality of the data.
- Automation and Monitoring: Automate repetitive data tasks, build monitoring solutions for data pipelines, and ensure data quality by implementing testing and validation protocols.
- Collaboration: Work with data scientists, analysts, and other stakeholders to understand data requirements and deliver solutions that meet business needs.
- Documentation: Ensure proper documentation of all processes and data pipelines to ensure maintainability and scalability.
Qualifications
- Education: Bachelor's or Master's degree in Computer Science, Information Systems, Engineering, or a related field.
- Experience: 3+ years of experience in data engineering, ETL development, or a similar role.
- Technical Skills:
- Languages: Proficient in SQL, Python, or Java/Scala.
- ETL Tools: Experience with ETL tools such as Apache Airflow, AWS Glue, Talend, or Apache NiFi.
- Cloud Platforms: Hands-on experience with cloud data platforms (e.g., AWS, Google Cloud, Azure).
- Data Warehousing: Familiarity with modern data warehousing systems (e.g., Snowflake, BigQuery, Redshift).
- Big Data: Experience with big data processing tools such as Apache Hadoop, Spark, or Flink.
- Data Streaming: Knowledge of data streaming technologies like Kafka, Kinesis, or Pub/Sub.
- Version Control: Experience with version control systems (e.g., Git).
- Problem Solving: Strong analytical and problem-solving skills with a focus on data quality and reliability.
- Communication: Excellent communication skills in French and English and the ability to work in a team environment.
- Agility: Ability to work in a fast-paced, agile environment with changing priorities.
Nice-to-Haves
- Experience with Kubernetes and Docker for containerized data processing.
- Familiarity with Terraform or other Infrastructure-as-Code (IaC) tools for automating cloud infrastructure.
- Knowledge of machine learning pipelines and AI data workflows.
