Global Head of Data Platform Engineering, SVP

About this role

Who we are looking for Design, build, and operate enterprise-grade, AI-ready data platforms at scale , using modern engineering, Agile, and Site Reliability Engineering (SRE) practices—enabling secure, resilient, and high-performance data capabilities across all State Street businesses and functions. The Head of Data Platform Engineering is accountable for delivering and operating industrial-strength data platforms that power State Street’s Data & AI ecosystem. This is a deep, hands-on engineering leadership role , leading a global organization of 100+ engineers to build and run mission-critical data platforms across cloud and on-premise environments. The role combines: • Strong engineering execution (distributed systems, data platforms) • Modern product mindset (platforms as products, user-centric design) • SRE discipline (reliability, observability, SLAs/SLOs) • Agile delivery models (iterative, outcome-driven execution)

The role partners closely with: • Data Architecture → implements the target-state blueprint • Strategy & Portfolio  → aligns to priorities and roadmap • Governance → ensures platforms enable compliant usage • AI Platform Engineering → provides foundational data capabilities

This leader is central to building a unified, scalable data platform ecosystem supporting Investment Services, Investment Management, Wealth, Alpha, Global Markets, and control functions. Success is measured by platform reliability, scalability, adoption, engineering velocity, and ability to power enterprise data and AI use cases .

What you will be responsible for

Enterprise Data Platform Engineering • Design, build, and operate data platforms as enterprise products , including: • Data ingestion and integration platforms • Data lake / warehouse / lakehouse architectures • Batch and streaming data processing • Data access and serving layers

• Own full platform lifecycle: • Engineering and build • Deployment and operations • Continuous improvement and optimization

Large-Scale Engineering Leadership (100+ Organization) • Lead and scale a global engineering organization of 100+ professionals across: • Platform engineering • Data engineering • Reliability engineering

• Build a strong leadership structure across: • Engineering domains (ingestion, processing, storage, serving) • Platform services and developer experience

• Drive a culture of: • Engineering excellence • Accountability and ownership • Automation and operational rigor

Site Reliability Engineering (SRE) & Operational Excellence • Establish and embed SRE practices across all data platforms, including: • SLAs, SLOs, and error budgets • Observability (metrics, logs, tracing) • Incident management and postmortems

• Ensure platforms meet enterprise standards for: • Availability and uptime • Performance and latency • Resilience and disaster recovery

• Drive automation of operations to minimize manual intervention

Agile Delivery & Product-Centric Engineering • Implement modern Agile delivery models across platform teams • Operate platforms with a product mindset , including: • Clear product definitions and roadmaps • Continuous delivery and iteration • Customer (developer/user) feedback loops

• Establish disciplined practices for: • Backlog management • Sprint planning and execution • Outcome-based delivery tracking

Cloud, Hybrid & On-Prem Platform Engineering • Architect and operate cloud-native and hybrid data platforms , including: • Public cloud environments • On-premise and private cloud systems

• Ensure seamless interoperability across environments • Optimize for: • Scalability and elasticity • Cost efficiency • Performance and reliability

Alignment to Enterprise Data Architecture • Implement the enterprise data architecture in platform design • Enable: • Standardized data domains • Interoperable data models • Consistent enterprise data flows

• Ensure platforms support reuse-first, domain-driven design

Reusable Data Products & Data Services • Enable and operationalize enterprise data product capabilities , including: • Reusable datasets • Domain-based data products • Discoverable and accessible data services

• Drive adoption of reusable assets across: • Investment Services, Investment Management, Wealth • Alpha platform, Markets, and control functions

Integration with AI Platform Engineering • Provide AI-ready data capabilities , including: • Structured and unstructured data pipelines • Feature engineering pipelines • Support for vector and embedding-based data

• Integrate seamlessly with AI Platform Engineering

Developer Experience & Platform Adoption • Build self-service, developer-friendly platforms , including: • APIs, SDKs, and tooling • Simplified onboarding and data access

• Improve productivity for: • Data engineers • Data scientists • Application teams

Platform Modernization & Rationalization • Identify and eliminate: • Redundant and legacy data platforms • Fragmented data pipelines

• Lead migration to: • Standardized enterprise platforms • Scalable, modern data architectures

• Align modernization with architecture roadmap and portfolio priorities

Enterprise Technology Collaboration • Partner with: • Global Technology Services (GTS) • Cloud and infrastructure teams • Cyber security and resilience engineering

• Ensure alignment with enterprise standards for: • Security • Reliability • Operational excellence

Qualifications & Experience • Senior leadership experience managing large-scale (100+) engineering organizations • Deep hands-on expertise in: • Data platforms and distributed systems • Cloud-native and hybrid architectures • Large-scale data processing (batch and streaming)

• Proven experience building and operating mission-critical platforms at enterprise scale • Strong experience implementing: • SRE practices and operational models • Agile engineering and product delivery frameworks

• Preferred background in: • Financial services or similarly regulated, data-intensive environments

Leadership Profile

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