Data Analytics and AI Development and Operations Senior Engineer

Company Description

Tradeweb is a global leader in electronic trading for rates, credit, equities, and money markets. As financial markets become increasingly interconnected, our technology enables efficient, multi-asset trading on a global scale. We serve more than 3,000 clients in more than 85 countries, including many of the world's largest banks, asset managers, hedge funds, insurers, corporations, and wealth managers.

Creative collaboration and sharp client focus have helped fuel our organic growth. We facilitated average daily trading volume (ADV) of more than $2.2 trillion over the past four fiscal quarters, topping $2.5 trillion in ADV for the first quarter of 2025.

Since our IPO in 2019, Tradeweb has completed four acquisitions and doubled our revenues – and 2024 was our 25th consecutive year of record revenues.

Tradeweb is a great place to work, recognized in 2024 by Forbes as one of America's Best Companies and by U.S. News & World Report as one of the Best Financial Services Companies to Work For.

Tradeweb Markets LLC ("Tradeweb") is proud to be an EEO Minorities/Females/Protected Veterans/Disabled/Affirmative Action Employer.

Mission: Move first and never stop. Collaborate with clients to create and build solutions that drive efficiency, connectivity, and transparency in electronic trading.

Group Details

Tradeweb is seeking a Data Analytics and AI Development and Operations Senior Engineer to join its Internal Audit Department, supporting the Data Analytics and AI Development and Operations Lead in building and maintaining scalable audit technology solutions. This role plays a critical part in operationalizing AI/ML models and data workflows that enhance the efficiency and effectiveness of business and technology audits.

We are seeking an hybrid Full-Stack Data Systems Engineer to support the deployment and operationalization of AI/ML models and data pipelines to enable the audit software platform. This hands-on role combines practical engineering with implementation rigor, focusing on converting audit prototypes into reliable, reusable applications. Core responsibilities include building and maintaining data pipelines, assisting with model deployment workflows, and contributing to system reliability through CI/CD, monitoring, and automation. The engineer will work closely with audit professionals to streamline repetitive tasks, promote software reusability, and uphold operational excellence. While not the primary architect, this role plays a critical part in executing the technical foundation of data, ML, and infrastructure components.

Success in this role will be measured by the strength and reliability of the data pipelines and the usability of the audit software platform. On the data side, key metrics include confidentiality (ensuring sensitive audit data is protected), accessibility (making data readily available to authorized users), and integrity (maintaining accuracy and consistency across workflows). On the software side, success will be defined by a low-touch, intuitive user interface that enables self-service capabilities for auditors, minimizes manual intervention, and maintains high uptime and responsiveness. Every engineering effort should contribute to a secure, seamless, and resilient platform that empowers audit teams to operate efficiently and independently.

Job Responsibilities

Support the Data Analytics and AI Development and Operations Lead in executing the team's vision and contributing to platform development initiatives.

Collaborate with India's team.

Implement components of the system architecture for scalable audit automation and AI platforms, based on guidance from the Lead.

Contribute to Buy vs Build evaluations by providing technical feasibility assessments and implementation effort estimates.

Implement reusable, production-grade audit software components.

Build and maintain robust data pipelines and AI/ML workflows to support audit analytics and reporting.

Deploy and operationalize machine learning models with a focus on reliability, performance, and maintainability.

Ensure operational stability through CI/CD automation, system monitoring, and proactive troubleshooting.

Drive best practices in software development, testing, deployment, and documentation.

Collaborate with auditors and business team members and fellow analytics team members to validate any assumption taken during the implementation and to validate the completeness and accuracy of the technical solutions deployed.

Understand and leverage Tradeweb's technology stack to maximize integration and reduce the platform technology risk/debt.

Apply a pragmatic, ROI-driven approach to the technology selection—leveraging advanced solutions only when justified.

Promote a "parsimonious" approach to data load – only retrieving and storing the information required.

Facilitate the compliance of the data stack with the Data Policies of Tradeweb.

Document system designs, deployment processes, and operational procedures for long-term sustainability.

Qualifications

5+ years of proven experience in cross-regional collaboration in the financial industry.

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

Strong proficiency in Python, SQL, and one or more modern programming languages.

Experience with data pipeline orchestration tools, rest API and ETL frameworks (Snowflakes).

Familiarity with ML model deployment.

Excellent communication skills and a high degree of autonomy, with the ability to collaborate effectively both the local team and with a core team based in New York and operate seamlessly across time zones.

Comfortable working in a remote, globally distributed environment with accountability and initiative.

Expertise in software development best practices, including modular design, code reusability, testing, and documentation.

Hands-on experience with SQL and NoSQL databases, data modeling, and performance optimization for audit and analytics use cases.

Wide range of technology exposure including:

multiple programming language : Python, C++, Javascript, VBA

available data tools for visualization Tableau, PowerBi, HTML

databases: SQL, Snowflakes

AI/Ml platform : KNIME, ALTERYX

code and no code development tools