below marketmost similar roles pay hereabove market
This role pays less than
68%
of similar roles. Most pay
$158,272–$225,198
— the blue band above.
At the midpoint, this role pays about
$161k
versus about
$192k
for comparable roles.
Based on 240 similar postings.
Employer
About State Street
State Street Corporation is one of the world''s largest custodian banks and asset managers, providing investment servicing, investment management, and investment research to institutional investors. Industry: Financial Services & Asset Custody
State Street currently has
351 open roles
on FindRole.
Listed pay typically runs
$115,000–$195,944
across 199 roles with salary data.
The Data Platform Engineer, VP II - State Street Investment Management joins the Data, Analytics & AI Services team to lead architecture, engineering execution, and modernization strategy for the investment management business. This role involves building a unified, cloud-native data platform covering ingestion, transformation, storage, and governance. Key responsibilities include designing Iceberg-based tables, establishing CI/CD and IaC practices using Terraform or CloudFormation, and developing curated semantic layer models. The candidate must utilize Python, SQL, Spark, Snowflake, and Databricks on AWS or Azure. Technical requirements include expertise in distributed computing, open table formats, and zero-copy data sharing. The role solves complex data engineering problems by ensuring reliability, scalability, and observability for high-concurrency analytical workloads, OLAP, and near real-time processing within a financial services context.
Execute the long-term vision and roadmap for a unified, cloud-native data platform.
Design and optimize Iceberg-based tables for performant analytical access across multiple compute engines.
Establish data engineering excellence through CI/CD, Infrastructure as Code, and automated testing.
Define data semantics and product contracts with stakeholders to ensure governance and compliance.
Build and publish curated semantic layer models and governed BI endpoints.
Optimize pipeline and query performance through partitioning, caching, and data lifecycle patterns.
Advance the use of agentic AI frameworks to improve software delivery across the SDLC.
What we're looking for
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, with the ability to design, debug, and optimize distributed data processing.
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).
Working knowledge of open table formats and metadata ecosystems, including Apache Iceberg and table catalogs/governance services.
Extensive hands-on experience architecting and implementing enterprise DevOps and CI/CD pipelines with cloud-agnostic solutions across public cloud providers.
Expertise in zero-copy data sharing, data virtualization, catalog federation, and governed data access patterns.
Data Product Management
Agile
Software Delivery
APIs
AI
Data Platforms
Solution Design
Product Roadmap
Backlog Management
Intelligent Automation
Data Governance
PL/SQL
Autosys
Unix
Linux
Shell Scripting
Windows
ITSM
Incident Management
Problem Management
Capacity Management
Database Administration
Production Support
AI
Automation