Overview of the Role:
Citi, the leading global bank, has approximately 200 million customer accounts and does business in more than 160 countries and jurisdictions. Citi provides consumers, corporations, governments, and institutions with a broad range of financial products and services, including consumer banking and credit, corporate and investment banking, securities brokerage, transaction services, and wealth management.
As a bank with a brain and a soul, Citi creates economic value that is systemically responsible and in our clients’ best interests. As a financial institution that touches every region of the world and every sector that shapes your daily life, our Enterprise Operations & Technology teams are charged with a mission that rivals any large tech company. Our technology solutions are the foundations of everything we do from keeping the bank safe, managing global resources, and providing the technical tools our workers need to be successful to designing our digital architecture and ensuring our platforms provide a first-class customer experience. We reimagine client and partner experiences to deliver excellence through secure, reliable, and efficient services.
Our commitment to diversity includes a workforce that represents the clients we serve from all walks of life, backgrounds, and origins. We foster an environment where the best people want to work. We value and demand respect for others, promote individuals based on merit, and ensure opportunities for personal development are widely available to all. Ideal candidates are innovators with well-rounded backgrounds who bring their authentic selves to work and complement our culture of delivering results with pride. If you are a problem solver who seeks passion in your work, come join us. We’ll enable growth and progress together.
The Data Analytics Engineer is a hands-on technical role within the Data Analytics team, responsible for designing, building, and optimizing scalable data pipelines, data transformation frameworks, and analytics solutions across distributed cloud and enterprise big data platforms. This position combines core data engineering expertise in Python, PySpark, Snowflake, Databricks, and Hadoop with a modern engineering mindset that leverages AI Coding Agents and developer tools to accelerate delivery and maintain high code quality. The role requires close collaboration with business analysts, data scientists, and functional stakeholders to translate complex business challenges into resilient, high-performance data products. Candidates should bring 2+ years of progressive experience in data engineering or big data analytics, along with strong analytical thinking, problem-solving, and cross-functional communication skills. This opportunity offers exposure to large-scale legacy-to-cloud data migration efforts alongside the adoption of next-generation, AI-augmented engineering practices.
Responsibilities:
* Design, implement, and maintain robust, end-to-end data pipelines for batch and real-time processing using Python, PySpark, Databricks, and Snowflake.
* Manage, migrate, and optimize legacy datasets and workloads across enterprise Hadoop ecosystems to modern cloud data platforms.
* Optimize pipeline performance, query execution times, resource allocation, and storage costs across distributed environments.
* Partner with business analysts, data scientists, and functional stakeholders to understand requirements and formulate analytical approaches. Deliver actionable insights that address complex business challenges.
* Apply structured root-cause analysis and analytical problem-solving techniques to diagnose data anomalies, pipeline bottlenecks, and complex logic issues.
* Create automated data validation, reconciliation, and quality monitoring frameworks to ensure high data integrity and compliance.
* Actively incorporate next-generation AI Coding Agents and developer assistants, such as Devin, GitHub Copilot, and Cursor, into daily development,…