Citi is looking for a Lead Data Engineer – AI & Distributed Analytics to architect and scale the enterprise data platforms that power our advanced analytics, machine learning, and Generative AI capabilities. In this high-impact role, you will lead a team of data engineers to design and deliver production-grade, high-performance data pipelines that bridge raw data and intelligent applications across the enterprise. You will work at the forefront of data innovation, collaborating directly with Data Scientists, AI Researchers, Product Managers, and Enterprise Architects to shape the technical direction of Citi's data ecosystem.
Responsibilities
- Define and drive the technical vision, architecture, and roadmap for Citi's enterprise Data Lake and Analytics platform, ensuring alignment with cloud-native and hybrid-cloud strategies.
- Lead, mentor, and develop a team of data engineers, setting technical standards and fostering a culture of continuous learning and agile delivery.
- Design and build robust batch and real-time streaming data pipelines using technologies such as Apache Iceberg, Starburst, Startree/Apache Pinot, Apache Kafka, and Apache Flink to support enterprise-scale data processing.
- Architect and implement Feature Stores to standardize feature engineering and ensure consistent, reliable data delivery for model training and real-time ML inference.
- Optimize data storage and query performance across Enterprise Data Lakes and Lakehouses, leveraging platforms such as Delta Lake, Apache Iceberg, and Databricks.
- Implement end-to-end metadata management and data lineage tracking to provide full visibility into how data flows from source systems through to AI models and analytics consumers.
- Establish automated data quality frameworks — including validation rules, reconciliation checks, and compliance controls — to ensure all pipelines meet enterprise security standards and data privacy regulations such as GDPR and CCPA.
- Evaluate and prototype emerging data technologies and frameworks, translating findings into actionable recommendations that keep Citi's data platform at the cutting edge.
Required qualifications & skills
- Bachelor's degree, university degree, or equivalent professional experience in Computer Science, Data Engineering, Information Systems, or a quantitative field.
- 6 or more years of professional experience in data engineering, software engineering, or data platform development, including 3 or more years in a technical lead or engineering leadership role.
- Expert-level proficiency in Python and SQL, with hands-on experience designing and delivering large-scale, distributed data systems.
- Deep expertise in big data frameworks including Apache Iceberg, Startree/Apache Pinot, and Hive/HDFS, with a strong command of distributed computing principles.
- Hands-on experience building and operating real-time streaming pipelines using Apache Kafka and Apache Flink at high volume and scale.
- Demonstrated ability to design and build data infrastructure specifically supporting machine learning, advanced analytics, or AI applications in production environments.
- Experience working with modern table formats and transactional storage layers such as Delta Lake, Apache Iceberg, or Apache Hudi.
- Solid experience with workflow orchestration tools such as Apache Airflow or equivalent modern orchestrators to manage complex pipeline dependencies.
Beneficial skills & qualifications
- Master's degree in Computer Science, Data Engineering, Information Systems, or a related quantitative discipline.
- Additional programming proficiency in Java
- Experience with cloud data platforms such as Databricks, including cluster tuning and lakehouse architecture optimization.
- Familiarity with vector databases — such as Milvus, Pinecone, Qdrant, or Chroma — for GenAI and large language model use cases.
- Understanding of dimensional modeling, Data Vault, and schema-on-read/write design patterns for enterprise analytics workloads.
- Hands-on experience with containerization and infrastructure tooling including Docker, Kubernetes, and Terraform for Infrastructure as Code.
What we offer
At Citi, you will work at genuine scale — building data infrastructure that directly shapes how one of the world's leading financial institutions leverages AI and analytics. This is a role where your technical decisions carry real weight, your leadership develops the next generation of data engineers, and your work delivers measurable impact across the enterprise.
- Hybrid working model with 3 days in the office and 2 days working remotely, giving you flexibility alongside strong team collaboration.
- Opportunity to lead high-impact, enterprise-scale data engineering initiatives at the intersection of big data, machine learning, and Generative AI.
- Access to continuous learning and professional development resources to keep your technical skills at the forefront of the industry.
- A performance-driven environment where your contributions are recognized and directly tied to your career progression.
- Competitive compensation and a comprehensive financial wellbeing package reflective of the seniority and strategic importance of the role.
- Wellbeing support and work-life balance programmes designed to help you perform at your best inside and outside of work.
- Collaboration with world-class Data Scientists, AI Researchers, and Enterprise Architects on problems of genuine global scale.
Build the data foundation that powers Citi's AI-driven future — apply today and take ownership of one of the most strategically important engineering platforms in global financial services.