Big Data PySpark Lead Engineer - Vice President

The Big Data PySpark Lead Engineer is a senior level position responsible for establishing and implementing new or revised application systems and programs in coordination with the Technology team. The overall objective of this role is to lead applications systems analysis and programming activities. Partner with multiple management teams to ensure appropriate integration of functions to meet goals as well as identify and define necessary system enhancements to deploy new products and process improvements Resolve variety of high impact problems/projects through in-depth evaluation of complex business processes, system processes, and industry standards Provide expertise in area and advanced knowledge of applications programming and ensure application design adheres to the overall architecture blueprint Utilize advanced knowledge of system flow and develop standards for coding, testing, debugging, and implementation Develop comprehensive knowledge of how areas of business, such as architecture and infrastructure, integrate to accomplish business goals Provide in-depth analysis with interpretive thinking to define issues and develop innovative solutions Serve as advisor or coach to mid-level developers and analysts, allocating work as necessary Appropriately assess risk when business decisions are made, demonstrating particular consideration for the firm's reputation and safeguarding Citigroup, its clients and assets, by driving compliance with applicable laws, rules and regulations, adhering to Policy, applying sound ethical judgment regarding personal behavior, conduct and business practices, and escalating, managing and reporting control issues with transparency.

Build and maintain scalable data pipelines using PySpark within a Big Data environment to process and transform large volumes of structured and unstructured data. Design and develop solutions across the Hadoop ecosystem — including Hive, HDFS, Sqoop, Spark, Impala, and Scala — to enable efficient data ingestion, processing, and storage. Develop and manage real-time and batch data workflows using streaming data platforms, ensuring high availability and low-latency data delivery. Write complex SQL queries to extract, validate, and analyze data across distributed systems, supporting data-driven decision-making. Design and implement data models and data architecture patterns aligned with data warehouse principles, ensuring scalability, accuracy, and consistency. Automate pipeline scheduling and orchestration using shell scripting and Autosys, reducing manual intervention and improving operational reliability. Independently identify, assess, and resolve technical risks and data issues in a timely manner, maintaining system integrity across the data platform.   Required Qualifications & Skills Hands-on expertise in PySpark and Big Data processing, with the ability to build and optimize distributed data workflows at scale. Practical knowledge of the Hadoop ecosystem, including Hive, HDFS, Sqoop, Spark, Impala, and Scala, applied in a production environment. Proficiency in complex SQL query development for data analysis, transformation, and validation across large datasets. Solid understanding of distributed systems architecture and how data flows across interconnected processing layers. Demonstrated knowledge of data modelling and data design, with familiarity in data warehouse concepts and dimensional modelling techniques. Competence in shell scripting and job scheduling using Autosys or equivalent workflow automation tools. Strong analytical and problem-solving ability, with a track record of working independently to diagnose and resolve complex data engineering challenges. Clear and effective communication skills, with the ability to articulate technical concepts to both technical and non-technical audiences.   Beneficial Skills & Qualifications Familiarity with streaming data platforms such as Apache Kafka or equivalent real-time data processing