Data Engineer

CUBE is a global RegTech business defining and implementing the gold standard of regulatory intelligence for the financial services industry. We deliver our services through intuitive SaaS solutions, powered by AI, to simplify the complex and everchanging world of compliance for our clients.

Requirements

• Design & Development: Build robust ETL pipelines and scalable data solutions using Microsoft Fabric (Data Engineering, Data Factory, OneLake), Python, Apache Spark, and SSIS.
• Data Integration: Develop reliable data integration frameworks that consolidate structured and unstructured data from various sources, ensuring high-quality and consistent datasets.
• Data Processing & Transformation: Create and optimize data transformation logic using Python, Spark SQL, and PySpark to support complex analytical workloads.
• Infrastructure Optimization: Monitor, troubleshoot, and enhance the performance, scalability, and resilience of data pipelines and infrastructure.
• Collaboration: Work closely with data scientists, analysts, and business stakeholders to gather data requirements and deliver efficient and secure data solutions.
• Data Modeling: Design data models (relational and dimensional) that support operational processes and business reporting needs.
• Governance & Compliance: Implement and uphold data governance, quality, and security standards across all systems and processes.
• Documentation & Mentoring: Maintain thorough documentation of data architecture, pipelines, and workflows. Mentor junior data engineers and contribute to knowledge-sharing across the team.