• Design, build, maintain, and operationalize data pipelines for high volume and complex data using appropriate tools and practices in development, test, and production environments. • Collaborate within an agile, multi-disciplinary team to deliver optimal data integration and transformation solutions • AAnalyze data requirements (functional and non-functional) to develop and design robust, scalable automated, fault-tolerant data pipeline solutions for business and technology initiatives • Profile data to assess the accuracy and completeness of data sources and provide feedback in data gathering sessions • Develop and design data mappings, programs, routines, and SQL to acquire data from legacy, web, cloud, and purchased package environments into the analytics environment • Understand and apply the appropriate use of ELT, ETL, data virtualization, and other methods to optimize the balance of minimal data movement against performance, and mentor others on their appropriate use • Drive automation of data pipeline preparation and integration tasks to minimize manual and error-prone processes and improve productivity using modern data preparation, integration, and AI-enabled metadata management tools and techniques • Leverage auditing facilities that will enable monitoring of data quality to detect emerging issues. • Deploy transformation rules to cleanse against defined rules and standards • Participate in architecture, governance, and design reviews, identifying opportunities and making recommendations • Participate in health check assessments of the existing environment and evaluations of emerging technologies • Collaborate with architects to design and model application data structures, storage, and integration in accordance with enterprise-wide architecture standards across legacy, web, cloud, and purchased package environments

• Participate in the development and maintenance of data pipelines in Python • Support the use of Databricks for data processing and transformation • Contribute to the implementation of orchestration workflows with Airflow or Prefect • Assist with data integration in cloud environments (AWS) • Collaborate with the data team on projects focused on performance and scalability

• Design and implement end-to-end data solutions on Microsoft Azure • Develop scalable and efficient data architectures that support large-scale data processing and analytics workloads • Ensure high performance, security, and compliance within Azure data solutions • Work closely with business and technical teams to understand and translate data needs into robust, scalable data architecture solutions • Provide expertise and leadership to the development team implementing data engineering solutions • Collaborate with Data Scientists, Analysts, and other stakeholders to ensure data architectures align with business goals and data analysis requirements

• Define and own the target data architecture and migration strategy for moving data infrastructure from AWS to Azure • Design end-to-end cloud-native data architectures leveraging Azure services, Snowflake, Databricks, and modern ETL/ELT and orchestration frameworks • Establish architectural principles, standards, and reference architectures covering: Data ingestion, transformation, and orchestration, analytical and research data platforms, security, access control, and compliance, scalability, performance, and cost optimization • Lead architectural decision-making across batch and streaming data pipelines, supporting both operational and research workloads • Partner closely with data engineers, analytics teams, and investment stakeholders to ensure architecture aligns with business and research needs • Guide and review solution designs, ensuring adherence to architectural standards and best practices • Define approaches for data modeling, metadata management, lineage, and data quality across the platform • Support integration of financial and market data providers (e.g., Bloomberg, LSEG, MSCI, FactSet) into the target architecture • Act as a trusted technical advisor to senior client stakeholders, translating complex technical concepts into clear architectural guidance • Engage directly with C-level executives and senior leadership to shape data strategy, investment in platforms, and long-term architectural direction • Lead executive-level architecture discussions and design reviews, presenting trade-offs, risks, costs, and benefits to support informed decision-making • Translate business, investment, and research objectives into clear data architecture strategies, ensuring alignment between executive priorities and technical execution • Serve as a senior architectural voice in governance forums, influencing platform roadmaps, cloud strategy, and data operating models • Contribute to delivery planning, risk management, and governance within Agile and change programs • Mentor engineers and architects, promoting architectural consistency and engineering excellence