Data Analytics Engineer (Treasury Data Science & Analytics)

Join our Treasury Data Science & Analytics team as a Data Analytics Engineer. In this hands-on role, you will design, develop, and maintain analytical data models and processing pipelines. You will take ownership of migration deliverables, establish consistent practices for pipeline development and maintenance, and work closely with finance business stakeholders. You will also build reusable components, strengthen data quality controls, and improve the reliability of existing solutions. This position offers a range of benefits, including health insurance, paid time off, and opportunities for career growth.

• Contribuer à la conception, au développement et à la maintenance de modèles de données analytiques, de pipelines de traitement et d'actifs de données réutilisables.
• Assumer la responsabilité des livrables de migration définis et aider à établir des pratiques cohérentes pour le développement, le déploiement, la planification, la surveillance et la maintenance des pipelines.
• Travailler en étroite collaboration avec les parties prenantes commerciales pour comprendre leurs besoins et traduire ces besoins en solutions techniques.

• Experience working with cloud services for data processing or storage, and practical experience deploying or troubleshooting containerized workloads within existing Docker/Kubernetes environments
• 1–3 years of relevant experience in Data Analytics Engineering, Analytics Engineering, Data Engineering, Data Warehousing or a related data or software role
• Programming experience in Python, with the ability to write clear, maintainable and reusable code
• Practical experience with analytical data modelling, including data granularity, relationships, aggregation and historical data
• Understanding of data governance and security fundamentals, including documentation, traceability and access controls
• Experience implementing automated data quality checks and validating outputs against source data
• Experience working with relational databases or cloud data warehouses; familiarity with Snowflake is beneficial
• Experience using Git, code reviews, automated testing and existing CI/CD pipelines
• Bachelor’s degree or higher in Computer Science or a related field, or equivalent professional experience
• Strong SQL skills, including combining datasets, implementing transformation logic and investigating discrepancies
• Stakeholder-management and communication skills, including the ability to explain complex technical decisions and trade-offs
• Experience building data transformation pipelines using dbt or a comparable framework
• Experience exploring and visualizing data and explaining findings to business stakeholders
• Experience supporting scheduled workflows and using logs, monitoring and alerts to investigate issues
• SQL-based data transformation using dbt or a comparable framework; Python programming
• Practical experience with cloud services and containerized workloads using Docker/Kubernetes
• Data exploration, visualization and stakeholder communication
• Data quality controls and reconciliation
• Data pipeline design, API integration, workflow migration and orchestration
• Production monitoring, troubleshooting, performance and cost optimisation
• Basic understanding of statistical modelling and machine learning concepts
• Data governance, access controls and technical documentation
• Analytical data modelling and warehousing, including dimensional modelling
• Software engineering practices: Git, code reviews, automated testing and CI/CD
• Experience with Snowflake
• Distributed processing using Apache Spark through PySpark or Spark SQL
• Reporting, monitoring and analytical application tools: Tableau, Grafana, Streamlit or similar
• Snowflake semantic views or comparable semantic-layer technologies for self-service and AI-assisted analytics
• Experience preparing data for forecasting and predictive models