Senior Data Developer Tech Lead - Vice President

Senior Data Developer Tech Lead - Vice President

Working at Citi is far more than just a job. A career with us means joining a team of more than 230,000 dedicated people from around the globe. At Citi, you'll have the opportunity to grow your career, give back to your community and make a real impact.

Job Overview

We are strategically building an A-team of highly skilled and autonomous individuals. We are seeking a Senior Data Developer to join one of our small, co-located, high-performing squads. This role demands a hands-on player/coach who can design, implement, and manage robust, scalable, and real-time data solutions within our Hadoop ecosystem and streaming platforms. The ideal candidate will possess deep architectural expertise in big data technologies, including real-time data streaming with Kafka, and a proven track record of end-to-end project delivery within the banking domain.

We are looking for an AI-first thinker who is adept at leveraging cutting-edge AI coding tools to maximize efficiency and innovation. This role requires strong leadership in architectural decisions, proficiency in developing data ingestion, advanced analytics, and comprehensive reporting solutions, and active collaboration within agile, focused teams. The successful candidate will ensure optimal performance, security, and governance of our data infrastructure, with a keen focus on integrating and advancing AI/ML capabilities, while also coaching and raising the bar for others. High autonomy and agency are baseline expectations.

Responsibilities

• Lead the design and implementation of scalable and efficient Hadoop architecture solutions, ensuring alignment with enterprise data strategy and best practices for small, co-located teams.
• Drive the design, development, and management of complex Hadoop-based architectures and real-time streaming data pipelines, demonstrating high autonomy and end-to-end ownership.
• Design and implement robust real-time data streaming solutions using technologies like Apache Kafka, proactively identifying and integrating performance enhancements.
• Collaborate effectively within a small, agile squad, translating diverse data requirements from engineers and scientists into detailed technical specifications and architectural blueprints.
• Optimize Hadoop clusters and real-time streaming applications for performance, scalability, and efficient resource utilization, ensuring high throughput and low latency.
• Maintain and monitor the Hadoop and streaming infrastructure to ensure high availability, reliability, and data integrity, proactively identifying and resolving issues with minimal oversight.
• Implement comprehensive data security and governance policies, adhering to strict regulatory and compliance standards prevalent in the banking sector, with a deep understanding of their functional impact.
• Stay abreast of the latest advancements and emerging trends in big data technologies, real-time analytics, AI/ML, and AI coding tools, recommending and integrating innovative, AI-first solutions to raise the team's capabilities.
• Troubleshoot and resolve complex issues within the Hadoop ecosystem and real-time streaming platforms efficiently, acting as a player/coach for less experienced team members.
• Develop Spark-based solutions to support near real-time data ingestion, advanced analytics, and comprehensive reporting, demonstrating hands-on expertise.
• Lead the contribution to the implementation of AI/ML models within data pipelines, focusing on data preparation, feature engineering, and seamless model deployment in production environments, and coach others on best practices.
• Drive MLOps initiatives to streamline the machine learning lifecycle, including continuous integration, continuous delivery, and continuous training, emphasizing efficiency gains through AI-driven development.
• Take full ownership of end-to-end project delivery for data initiatives, from conceptualization and architectural design to implementation, testing, and deployment, ensuring projects are delivered on time, within scope, and with exceptional quality.

Recommended Qualifications

• 10+ years of proven experience in designing, implementing, and managing complex Hadoop-based architectures and real-time data platforms, with a strong emphasis on senior, hands-on contributions.
• Demonstrated architectural leadership experience in big data environments, capable of operating as a player/coach.
• Strong understanding of Hadoop ecosystem components, including HDFS, YARN, MapReduce, Hive, HBase, and Spark.
• Extensive hands-on experience with real-time data streaming technologies, particularly Apache Kafka, and a track record of optimizing their performance.
• Demonstrable proficiency and experience using AI coding tools for accelerated and efficient development.
• Strong hands-on and architectural knowledge of Python, PySpark, Unix, and SQL.
• Familiarity with major cloud platforms such as AWS, Azure, and Google Cloud.
• Experience with advanced data modeling principles and practices, including dimensional modeling and data vault.
• Proficiency in designing and implementing complex ETL/ELT processes for both batch and real-time data.
• In-depth knowledge of data warehousing concepts and best practices.
• Strong understanding and practical experience with AI/ML lifecycle management, MLOps practices, and the integration of machine learning models into data solutions, with an "AI-first" mindset.
• Experience with Generative AI (GenAI) concepts and tools, and the ability to leverage them in practical applications, is a significant advantage.
• Proven track record of successful end-to-end project delivery for complex data initiatives.
• Deep and strong domain understanding within the banking or financial services sector, with a clear ability to articulate the functional impact and importance of technical work, and adherence to relevant data standards and regulations.
• Excellent problem-solving skills and the ability to operate with high autonomy as part of a small, high-performing team.
• Strong communication skills, both written and verbal, with the ability to articulate complex technical concepts, mentor others, and raise the bar for the team.

Education

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.