Data Platform Engineer, VP 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 Vice President – Technology Lead to lead the architecture, engineering execution, modernization strategy, and technology roadmap supporting the investment management business. This role is accountable for the architecture, engineering delivery, and operational excellence of modern data platform and software capabilities that enable the investment management business. The successful candidate will bring deep hands-on expertise in large-scale data platform architecture, distributed computing, data pipeline engineering, and modern cloud architecture.

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

These skills will help you succeed in this role

• Execute the long-term vision, target architecture, and roadmap for a unified, cloud-native data platform spanning ingestion, transformation, storage, governance, and secure access at scale.
• Collaborate with enterprise and application architects to define data strategies and deliver logical/physical data models aligned to analytical workloads.
• Collaborate on the  on the core platform capabilities (Lakehouse, batch, events, streaming, metadata/catalog, observability, and security), ensuring reliability, scalability, and operability.
• Design, implement, and optimize Iceberg-based tables (partitioning, compaction, metadata) for consistent, performant analytical access across multiple compute engines.
• Establish data engineering excellence: CI/CD, IaC (Terraform/CloudFormation), automated testing, schema/versioning practices, data quality controls, and end-to-end observability.
• Partner with Product, Analytics, ML, and domain SMEs to define data semantics and data product contracts (schemas, SLAs, documentation, versioning/backward compatibility), sand to enforce governance, stewardship, and compliance standards.
• Build and publish curated semantic layer models (serving models/marts), exposing governed BI endpoints and/or consumption APIs to ensure consistent metrics and business definitions.
• Optimize pipeline and query performance through effective partitioning/clustering, caching, archiving, and data lifecycle (purge/retention) patterns.
• Evaluate and advance the use of agentic AI frameworks to improve software delivery across the full software development life cycle (SDLC).

Required Qualifications

• Master’s degree in Computer Science or a related engineering discipline.
• 15+ years of technology experience in the financial services industry.
• Strong hands-on programming in Python and SQL (Java/Scala a plus), with the ability to design, debug, and optimize distributed data processing; familiarity with AI-assisted development tools (e.g., GitHub Copilot, Codex, Anthropic tools).
• Deep experience with Spark (batch and streaming) including performance tuning, troubleshooting, and cost-aware optimization.
• Hands-on experience building and operating modern Lakehouse platforms using Snowflake and Databricks on AWS (Azure acceptable), supporting high concurrency analytical workloads.
• Working knowledge of open table formats and metadata ecosystems, including Apache Iceberg and table catalogs/governance services (e.g., Unity Catalog or equivalent).
• Demonstrated hands-on experience designing and implementing modern, open architecture data platforms supporting OLAP, OLTP, and near real-time workloads; familiarity with catalog services that enable multiple compute engines, high concurrency, and strong performance.
• Extensive hands-on experience architecting and implementing enterprise DevOps and CI/CD pipelines with cloudagnostic solutions across public cloud providers (e.g., AWS, GCP, Azure).
• Expertise in zero-copy data sharing, data virtualization, catalog federation, and governed data access patterns, enabling scalable and trusted data consumption across platforms.
• Experience working with both unstructured data sources and processing patterns