Senior Data Engineering Specialist (Hybrid)

We're seeking someone to join our Analytics & Reporting Fleet team as a Senior Data Engineering Specialist in Trade Enrichment Data Reporting & Allocations (TEDRA) to build and evolve large-scale data platforms and analytics solutions that process tens of billions of trade events daily and power award-winning trade latency monitoring and reporting capabilities across Institutional Securities Technology. In the Technology division, we leverage innovation to build the connections and capabilities that power our Firm, enabling our clients and colleagues to redefine markets and shape the future of our communities. This is a Lead Data & Analytics Engineering position at Director level, which is part of the job family responsible for providing specialist data analysis and expertise that drive decision-making and business insights as well as crafting data pipelines, implementing data models, and optimizing data processes for improved data accuracy and accessibility, including applying machine learning and AI-based techniques. Since 1935, Morgan Stanley is known as a global leader in financial services, always evolving and innovating to better serve our clients and our communities in more than 40 countries around the world. What you'll do in the role: Design and develop scalable data engineering solutions supporting mission-critical trade reporting, trade monitoring, and analytics platforms. Build and maintain high-performance database systems and data pipelines that process terabytes of data and tens of billions of records daily. Develop software solutions using Python, Java, C++, Perl, or similar technologies to automate data processing, operational workflows, and platform management. Build and optimize large-scale distributed data platforms leveraging technologies such as Snowflake, Databricks, SingleStore, Apache Spark, and related analytics ecosystems. Design data models, implement complex SQL solutions, and drive performance tuning initiatives to support demanding analytical and operational workloads. Lead technical planning and execution of platform migrations, database upgrades, capacity planning initiatives, and infrastructure modernization projects. Champion engineering excellence by identifying opportunities to modernize technology stacks, improve system architecture, and promote best practices in software development, data engineering, automation, observability, and operational resiliency. Influence technical direction across multiple platforms by building consensus among stakeholders, driving adoption of modern engineering approaches, and guiding teams through complex technology transformations. Support real-time and event-driven architectures using Kafka and related messaging technologies to enable low-latency processing and analytics. What you'll bring to the role: 6+ years of experience in designing, building, and supporting large-scale data engineering platforms and data-intensive applications in distributed environments. Experience in software development using Python, Java, C++, Perl, or similar programming languages, with a strong focus on engineering quality, maintainability, and automation. Understanding of relational database concepts, advanced SQL development, data modelling, performance tuning, and capacity planning. Experience with modern data platforms and analytics technologies such as Snowflake, Databricks, SingleStore, Apache Spark, or comparable distributed data processing ecosystems. Experience in leading complex technical initiatives, driving platform modernization efforts, and influencing adoption of engineering best practices across multiple teams and stakeholders. Ability to design and support scalable, resilient, and high-performance systems that process large volumes of data and support critical business functions. Experience with Linux-based environments, distributed systems, and event-driven architectures leveraging technologies such as Apache Kafka. All our positions are