SR. Data Engineer - Assistant Vice President

Responsibilities

• Design, develop, and maintain scalable and efficient data pipelines using Big Data technologies (e.g., Hadoop, Spark, Kafka, Hive, Parquet, Avro) to ingest, process, and transform large volumes of structured and unstructured data.
• Implement and optimize ETL/ELT processes for data ingestion, cleansing, transformation, and loading into data warehouses, data lakes, and other data stores.
• Integrates subject matter and industry expertise within a defined area.
• Applies in-depth understanding of how data engineering and analytics collectively integrate within the sub-function as well as coordinates and contributes to the objectives of the entire function.
• Build and maintain data pipelines outcomes as data federations layers for lambda and Data Mesh architecture using tools like Starburst with strategy for adopting AI techniques-based use cases to drive efficiency and reduce data copies (e.g., machine learning, deep learning, natural language processing) to extract insights, automate processes, and enhance decision‑making.
• Develop and deploy microservices‑based architectures to support data‑intensive applications and ensure scalability, resilience, and maintainability.
• Ensure data quality, integrity, and security throughout the data lifecycle.
• Contribute to the continuous improvement of data engineering processes, standards, and best practices.
• Effectively communicate technical concepts and solutions to both technical and non-technical audiences.
• 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, as well as effectively supervise the activity of others and create accountability with those who fail to maintain these standards.
• Conduct tasks related to feasibility studies, time and cost estimates, IT planning, risk technology, applications development, model development, and establish and implement new or revised applications systems and programs to meet specific business needs or user areas.
• Monitor and control all phases of development process and analysis, design, construction, testing, and implementation as well as provide user and operational support on applications to business users.
• Utilize in-depth specialty knowledge of applications development to analyze complex problems/issues, provide evaluation of business process, system process, and industry standards, and make evaluative judgement.
• Recommend and develop security measures in post implementation analysis of business usage to ensure successful system design and functionality.
• Consult with users/clients and other technology groups on issues, recommend advanced programming solutions, and install and assist customer exposure systems.
• Ensure essential procedures are followed and help define operating standards and processes.
• Has the ability to operate with a limited level of direct supervision.
• Can exercise independence of judgement and autonomy.
• 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.

• 8+ years overall experience in a Big Data or Enterprise large scale Apps development using scalable tools like Databricks, Scala, Java and python ecosystem.
• Minimum 5+ years of proven experience in a Data Engineering lead role with a strong focus on Big Data, data federation and data extractions data mesh architecture.
• Deep understanding of data modeling, data warehousing concepts, methodologies, and best practices.
• Expertise in designing, building, and maintaining data pipelines using Big Data…