Lead Data Engineer – Vice President

• Collaborate with business leaders, product owners, data scientists, and enterprise architects to translate complex business requirements into scalable technical solutions and platform capabilities.
• Provide hands-on technical leadership in the design, development, deployment, and support of enterprise-scale data engineering solutions. Hands-on development is required.
• 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, Databricks, and other modern data warehousing technologies.
• Implement end-to-end metadata management and data lineage tracking to provide full visibility into how data flows from source systems through transformations to analytics consumers and AI models.
• Establish automated data quality frameworks, including validation rules, reconciliation checks, monitoring, and controls to ensure high-fidelity data and compliance with enterprise governance standards.
• Ensure all data platforms and pipelines meet enterprise security requirements, encryption standards, and data privacy regulations such as GDPR and CCPA.
• Evaluate and prototype emerging data technologies, frameworks, and tools, translating findings into actionable recommendations that keep Citi's data platform at the cutting edge.
• Drive continuous improvement initiatives focused on platform scalability, reliability, operational excellence, and performance optimization across distributed data systems.
• Required qualifications & skills
• Bachelor's degree, university degree, or equivalent professional experience in Computer Science, Data Engineering, Information Systems, or a quantitative field.
• 6+ years of professional experience in data engineering, software engineering, or data platform development, including 3+ years in a technical lead or engineering leadership role.
• Expert-level proficiency in Java (required) and SQL, with additional expertise in Python highly desirable.
• Demonstrated expertise designing and building large-scale data transmission, ingestion, transformation, and processing pipelines using Java-based technologies.
• Deep expertise in big data frameworks including Apache Iceberg, Startree/Apache Pinot, Hive/HDFS, and 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 Apache Iceberg, Delta Lake, or Apache Hudi, with strong understanding of lakehouse architectures.
• Solid experience with workflow orchestration tools such as Apache Airflow or equivalent modern orchestrators to manage complex pipeline dependencies.
• Strong understanding of data governance, metadata management, data lineage, data quality frameworks, and enterprise security practices.
• Experience building and scaling high-volume distributed data lakes, including modern data orchestration, ingestion, processing, and distribution capabilities.
• Exceptional communication skills with the ability to articulate complex technical concepts and data architectures to both technical and non-technical stakeholders.
• Strong problem-solving capabilities with demonstrated success troubleshooting and optimizing complex distributed systems.
• Beneficial skills & qualifications

None flagged.