data engineer in financial services

Cantor Fitzgerald operates in the financial services industry.

• Build and maintain market, economic, research, news, and internal data pipelines, as well as Fenics/BGC data integration;
• Design and implement a scalable bronze/silver/gold data architecture to support AI and quant models;
• Establish and monitor data quality gates to ensure data integrity and accuracy;
• Develop and manage instrument, ticker, country, client, source, and theme metadata for effective data categorization and retrieval;
• Create APIs and tables for seamless integration with agent tools, dashboards, and quant models;
• Implement data lineage tracking, user entitlements, and permissions management systems;
• Monitor and optimize data pipelines for performance and efficiency, ensuring smooth data flow;
• Collaborate with data scientists and analysts to understand their data needs and provide model-ready datasets;
• Stay updated with the latest trends in time-series and complex-data analysis, incorporating best practices into the data platform;
• Ensure data security and privacy compliance, adhering to industry standards and regulations.

• Strong proficiency in Python and SQL, with experience in production-level ETL/ELT processes;
• In-depth knowledge of cloud data stack technologies, such as Azure, Databricks, Delta Lake, or Snowflake;
• Experience with time-series data and complex data structures, demonstrating an understanding of data quality and monitoring;
• Ability to clean and transform messy data into structured, model-ready datasets, ensuring data integrity;
• Excellent problem-solving and analytical skills, with a keen eye for detail and data accuracy;
• Strong communication and collaboration skills, able to work effectively with cross-functional teams;
• A proactive and self-motivated approach to work, with a passion for data engineering and continuous improvement;
• 2+ Years of relevant work experience in data engineering or a similar role;
• Nice to have: Familiarity with ticker-level data processing and experience with kdb+ or similar systems, a bachelor's degree in Computer Science, Engineering, or a related field.

• No conditions specified