Data Platform Engineer, Vice President II - State Street Investment Management

Who we are looking for  State Street Investment Management’s Data, Analytics & AI Services (DAAIS) team is seeking an experienced hands-on architect, engineer for building the multi-cloud open architecture-based data platform that delivers trusted data products to the business functions.  This role is an 80% IC role in doing hands-on engineering and developing POCs to support a resilient, scalable, performance optimized, secure multi-cloud data platform, and 20% of the capacity is responsible for managing and mentoring the employees, based out of Bangalore, responsible for the production maintenance & support of the legacy data platform. The successful candidate will bring deep hands-on expertise in large-scale data platform architecture, distributed computing, data pipeline engineering, and modern cloud architecture and operational experience.  Why this role is important to us The team you will be joining is a part of State Street Investment Management, one of the largest asset managers in the world. We partner with many of the world’s largest, most sophisticated investors and financial intermediaries to help them reach their goals through a rigorous, research-driven investment process. With over four decades of experience and trillions of dollars in assets under management, we offer one of the broadest selections of services across asset classes, risk profiles, regions and styles. As pioneers in index, ETF, and ESG investing, we are always inventing new ways to invest.  Join us if making your mark in the asset management industry from day one is a challenge you are up for.  What you will be responsible for  As a Data Platform Engineer, Vice President II, you will - Validate target data platform architectures and design principles through hands-on engineering, technical spikes, and POCs for a unified, cloud-native Lakehouse.  Partner with architects to translate reference architectures and data strategies into working prototypes, measurable validation criteria, and production-ready implementation patterns.  Optimize scalable batch and streaming data pipelines using Python/Scala, SQL, Spark, and modern orchestration frameworks.  Engineer high-performance Iceberg-based data models and tables, including partitioning, compaction, schema evolution, metadata management, and multi-engine interoperability.  Establish data engineering standards for data quality, lineage, observability, reliability, versioning, and backward-compatible data product contracts.  Optimize platform, pipeline, and query performance through workload testing, partitioning and clustering, caching, concurrency management, and cost-aware lifecycle practices.  Lead capabilities from POC to production by defining non-functional requirements, resolving engineering risks, integrating with platform services, and enabling operational support and SLA readiness.