Head of Data Platform Engineering

Citi•Jersey City, NJ

1d•$170,000 - $300,000•Onsite

About The Position

The Institutional Data Platform (IDP) team within the Enterprise Data Solutions function has built a next-generation Data Fabric to solve the evolving Business, Analytical, and Regulatory needs of Citi's Markets business. This Head of Data Platform Engineering role is a senior Director-level position based in Jersey City, NJ, responsible for the delivery and management of a broad technology portfolio within the Institutional Data Platform (IDP) function. The role involves leading a global team to drive large-scale data platform initiatives, delivering cross-market analytics solutions for Citi's Markets business. It requires close collaboration with Front Office, Risk Managers, Business Stakeholders, and senior technology leadership to manage program scope, ensure delivery excellence, and drive innovation aligned with firm standards and best practices.

Requirements

15+ years of experience in large Financial Services Technology organizations
Degree in Computer Science, Engineering, or a related technical discipline
Deep expertise in data engineering, AI/ML, GenAI, MPP platforms, and regulatory reporting
Hands-on experience with ecosystems like Impala, Hive, Spark
Hands-on experience with databases such as Redshift, Snowflake, and Databricks
Proficiency in Python (and its data science ecosystem)
Proficiency in Java/Scala
Practical experience with Machine Learning techniques
Practical experience with Generative AI technologies like LangChain and Transformer Models
Experience with distributed computing (Apache Spark/Flink)
Experience with in-memory databases
Experience with public cloud platforms (AWS, GCP, Azure)
Demonstrated experience in building and leading high-performance, globally distributed engineering teams
Strong stakeholder management skills

Responsibilities

Lead the delivery of a technology portfolio, data platform initiatives, and regulatory reporting programs.
Manage program scope, risks, and stakeholder relationships.
Drive the design and development of large-scale, real-time data platforms.
Champion best practices in data architecture, data modeling, and cloud-native engineering.
Champion the adoption of GenAI, Machine Learning, and advanced analytics.
Partner with data science teams to embed responsible AI practices.
Ensure changes are managed with appropriate controls, documentation, and approvals.
Drive compliance with applicable laws, rules, and regulations.
Manage and develop multiple teams of technology professionals.
Maintain a strong focus on hiring and retaining top, diverse talent.