Big Data Developer - Vice President

The Big Data Developer is a senior level position responsible for establishing and implementing scalable, efficient big data application systems and platforms—primarily across Hadoop/Spark and cloud environments—in coordination with the Technology team. The overall objective of this role is to lead big data systems analysis, data engineering, and applications programming activities.
Responsibilities   Partner with multiple management teams to ensure appropriate integration of functions to meet goals, and to identify and define necessary platform and system enhancements to deploy new data products and process improvements.
Design and implement scalable and efficient Hadoop architecture solutions encompassing core ecosystem components, including HDFS, YARN, Map Reduce, Hive, HBase, and Spark.
Collaborate with data engineers, data scientists, and analytics stakeholders to understand data requirements and deliver robust, reliable pipelines and analytical datasets.
Develop Spark/PySpark solutions to support near real-time data ingestion, analytics, and reporting, ensuring high performance and reliability.
Optimize Hadoop and Spark clusters for performance and resource utilization, including capacity planning, tuning, and job orchestration best practices.
Maintain and monitor Hadoop infrastructure to ensure high availability, reliability, and observability; implement proactive alerting, logging, and issue resolution.
Implement and enforce data security and governance policies (e.g., access controls, encryption, data quality, lineage, and cataloging) across big data platforms.
Troubleshoot and resolve issues across the Hadoop ecosystem (jobs, services, resource management), driving root‑cause analysis and permanent fixes.
Provide expertise in the area and advanced knowledge of applications programming, ensuring application and data solution design adheres to the overall architecture blueprint and cloud reference patterns.
Utilize advanced knowledge of system flow to develop standards for coding, testing, debugging, deployment, and implementation—leveraging Python, PySpark, Unix/Linux, and SQL.
Develop comprehensive knowledge of how architecture, data platforms, and infrastructure integrate to accomplish business goals, including data modeling, ETL processes, data warehousing, and cloud‑native services (AWS, Azure, Google Cloud).
Provide in-depth analysis with interpretive thinking to define issues and develop innovative, scalable solutions aligned with business and regulatory requirements.
Serve as advisor or coach to mid‑level developers and analysts, allocating work as necessary and uplifting engineering practices through code reviews and mentorship.
Stay updated with the latest advancements in Hadoop/big data technologies and related areas; evaluate and introduce improvements, including AI/ML lifecycle management, MLOps, and GenAI‑adjacent integrations where appropriate.
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.
Recommended Qualifications   6+ years of relevant experience in Big Data/Application Development or systems analysis roles, including building and operating production‑grade data pipelines on Hadoop/Spark.
Extensive experience in system analysis and in programming of big data applications and data platforms.
Proven experience designing and managing Hadoop‑based architectures, including cluster configuration, resource management (YARN), and ecosystem integration.
Strong understanding and hands‑on expertise with the Hadoop ecosystem: HDFS, YARN, Map Reduce, Hive, HBase, and Spark.
Strong hands‑on and architectural knowledge of Python, PySpark, Unix/Linux, and SQL.

Experience with data modeling, ETL processes, and data warehousing concepts and implementation.
Experience implementing data security and governance (e.g., RBAC,…