Data Engineer

Master’s degree in Data Engineering, Computer Science, or a related technical field. 3–5 years of hands-on experience designing and maintaining large-scale data pipelines. Proven experience working with structured and unstructured financial data or other complex data domains. Experience collaborating in finance, consulting, or private equity environments is a must. Advanced proficiency in Python and SQL for data processing and transformation. Hands-on experience with cloud data platforms (e.g. AWS, Azure, GCP, or Databricks) and data workflow orchestration tools (e.g. Airflow, Prefect, or Dagster). Strong understanding of data modeling, schema design, and API integration. Experience with big data frameworks (Spark, Kafka, Delta Lake, etc.) is highly desirable. Familiarity with version control (Git) and DevOps practices (CI/CD pipelines). Strong communication skills and ability to work cross-functionally with non-technical stakeholders. Fluency in English; French is a plus.

Design, build, and maintain robust data pipelines and architectures to support private equity investment workflows. Ensure seamless ingestion, transformation, and integration of large, diverse datasets from multiple internal and external sources. Develop scalable data infrastructure, monitor and optimize data flow performance, reliability, and data quality. Partner with data scientists, investment professionals, and operations teams to operationalize analytics. Automate data workflows, implement CI/CD pipelines, and contribute to the continuous improvement of data engineering practices.