Join a team that connects data to real outcomes for a global financial institution. You will build scalable data applications, centralize critical datasets, and deliver analytics that help teams make faster, better decisions. If you enjoy solving complex problems end-to-end—from understanding the need to launching a working solution—this role offers a high-visibility opportunity to make an impact. You'll work across finance, product, operations, and technology to modernize how information is accessed and used. Job summary As a Data Solutions & Analytics Lead in the Securities Services Finance Data Solutions & Analytics team, you will build and manage data solutions that streamline reporting, enable automation, and improve how teams access and act on information. You will partner with stakeholders to define problems clearly, translate needs into requirements, and deliver production-ready solutions. You will work hands-on with large datasets, create data models, and deliver analytics that support decision-making. You will help establish best practices and contribute to a collaborative, solutions-oriented team culture. This role blends data analytics, product delivery, and project execution. You will help centralize key datasets in a data lake and develop reusable data assets that reduce fragmentation and manual effort. You'll have the opportunity to initiate artificial intelligence and machine learning use cases where they can improve process efficiency and decision-making. Success in this role comes from strong ownership, clear communication, and the ability to move from ambiguity to delivery. Job responsibilities Design and develop data models that integrate complex datasets and enable analytics at scale. Analyze data using tools such as SQL, Python, Tableau, Alteryx, or similar platforms to uncover insights and improve outcomes. Automate recurring reporting and manual processes to improve efficiency and reduce operational risk. Lead end-to-end project delivery, including requirements gathering, documentation, and stakeholder updates. Write clear business requirements documents and translate business needs into actionable delivery plans. Partner with product, operations, technology, and finance teams to align solutions to strategic goals. Build scalable data applications and data products from concept through implementation and adoption. Identify opportunities to apply artificial intelligence and machine learning to enhance decision-making and workflows. Establish and promote standards and best practices for analytics, documentation, and solution delivery. Present findings and recommendations to stakeholders in a clear, structured manner. Improve how teams access and use data by enabling self-service, trusted datasets, and transparent definitions. Required qualifications, capabilities, and skills Bachelor's degree. At least four years of experience in data analytics, product development, or project management (financial services preferred). Hands-on proficiency with at least one modern analytics, visualization, or engineering tool (for example: SQL, Python, Tableau, Power BI, R). Demonstrated experience working with large, complex datasets and building data models. Proven ability to deliver outcomes independently with strong ownership and follow-through. Strong problem-solving skills with a logical, structured approach to planning and decision-making. Demonstrated project execution skills, including writing requirements and managing delivery in a fast-paced environment. Strong communication skills, including presenting analysis and recommendations to cross-functional stakeholders. Experience partnering effectively across business and technology teams. Experience designing solutions that improve reporting, decision-making, or operational efficiency. Preferred qualifications, capabilities, and skills Experience supporting finance functions such as financial planning and analysis or finance business management. Experience in securities services, transaction processing, or related financial products. Experience delivering automation or advanced analytics initiatives that reduce manual effort. Experience initiating or delivering artificial intelligence and machine learning use cases. Familiarity with building data assets in a data lake environment. Relevant certifications in analytics, data, or finance.