The Principal Engineer will join the Retail and Wealth Risk Engineering team within the Enterprise Risk Technology platform. This intermediate-level position focuses on designing, building, and maintaining scalable data pipelines and solutions using Big Data, Databricks, and AI technologies. The ideal candidate will be passionate about data and analytics, playing a crucial role in enhancing business engagement and growth through innovative data and analytics platforms. This role requires a high-impact individual with strong problem-solving skills.

• 8+ years of overall experience in large-scale application development.
• Minimum of 5+ years of proven experience in a Data Engineering lead role focused on building enterprise-grade, high-volume ELT/ETL processes using the Spark and Databricks ecosystem.
• Hands-on experience with agentic AI development, including Devin.AI or Github Copilot, and integrating models via platforms like MCP using advanced prompt engineering.
• Proven experience developing and automating microservice integrations to support data-intensive applications.
• Proficiency in at least one programming language commonly used for data engineering, such as Python, Java, or Scala.
• Strong SQL skills and experience with various relational databases.
• Deep understanding of data modeling, data warehousing concepts, Data Mesh architecture, and data federation.
• Excellent communication, collaboration, and problem-solving skills.

• Design, develop, and maintain scalable, enterprise-grade ELT/ETL processes to handle large data volumes using the Spark and Databricks ecosystem.
• Implement and optimize data pipelines for ingestion, cleansing, transformation, and loading into data warehouses, data lakes, and other data stores.
• Build and maintain data federation layers for lambda and Data Mesh architectures using tools like Starburst.
• Develop, deploy, and automate microservice integrations to support data-intensive applications, ensuring scalability, resilience, and maintainability.
• Integrate and leverage agentic AI tools and platforms through advanced prompt engineering to enhance development and operational efficiency.
• Ensure data quality, integrity, and security throughout the entire data lifecycle.
• Contribute to the continuous improvement of data engineering processes, standards, and best practices within the team.
• Appropriately assess risk when business decisions are made, demonstrating consideration for the firm's reputation and compliance with applicable laws, rules, and regulations.

• Citi offers a comprehensive benefits package including competitive pay, stock programs, healthcare coverage, retirement plans, paid time off, parental leave, and programs supporting employee wellbeing and professional development.