Platform Engineering Lead, VP

Who we are looking for

We’re looking for a dynamic and forward-thinking leader who’s passionate about shaping the future of technology at scale. If you thrive at the intersection of innovation and execution, and enjoy turning cutting-edge trends into practical enterprise solutions, this role is for you. As a hands-on strategist, you’ll guide the development and evaluation of advanced capabilities across diverse business domains, while mentoring teams and influencing stakeholders. The ideal candidate brings a strong blend of technical depth and strategic vision, with a drive to build reusable frameworks, foster responsible practices, and cultivate a culture of experimentation and continuous learning.

Why this role is important to us

Our technology function, Global Technology Services (GTS), is vital to State Street and is the key enabler for our business to deliver data and insights to our clients. We’re driving the company’s digital transformation and expanding business capabilities using industry best practices and advanced technologies such as cloud, artificial intelligence and robotics process automation. 

What you will be responsible for

As Data Engineering Lead you will:

• Lead the design, development, and deployment of solutions across use cases such as document processing, intelligent agents, and automation frameworks
• Collaborate with cross-functional teams to translate business needs into data-driven workflows using modern orchestration tools
• Promote responsible practices including security, compliance, and explainability, while ensuring robust deployment
• Establish reusable frameworks, evaluation pipelines, and scalable components to accelerate adoption and maintain consistency
• Prioritize initiatives, manage delivery timelines, and align efforts with enterprise goals
• Define and evolve data architecture patterns for fund accounting, custody, and reference data domains, ensuring clear lineage from source systems through consumption layers
• Partner with business and technology stakeholders to model complex investment data concepts, including NAV, positions, transactions, cash, income, corporate actions, securities, accounts, and party/reference data
• Establish scalable data models, integration standards, and quality controls that support accurate accounting, custody servicing, reconciliation, regulatory reporting, and client-facing data delivery
• Drive architecture decisions that balance domain expertise, data governance, performance, security, and reuse across enterprise data platforms

What we value 

These skills will help you succeed in this role

• Solid understanding of modern data engineering concepts and emerging technologies
• Experience with Python and relevant libraries such as FastAPI, PyTorch, TensorFlow, and Pandas; Java experience is a plus
• Familiarity with distributed systems, orchestration tools, and API integration
• Strong foundation in software engineering principles including OOPS, design patterns, data structures, and algorithms
• Ability to manage and support multiple workflows or agents in parallel
• Awareness of responsible technology principles and practices, including fairness, transparency, and safety
• Experience building reusable components, libraries, and frameworks to accelerate development
• Comfort using tools like Fiddler and Postman for API development and debugging
• Deep understanding of fund accounting, custody, and reference data domains, with the ability to connect business concepts to scalable data architecture
• Strong data modeling skills across positions, transactions, cash, income, securities, accounts, and party/reference data
• Experience designing data lineage, data quality, reconciliation, and governance controls for regulated financial services platforms
• Ability to balance domain knowledge, engineering discipline, platform reuse, and client delivery needs in architecture decisions
• A proactive mindset with the ability to work independently and guide teams under general supervision

Education & Preferred Qualifications

·       Bachelor’s or advanced degree in Computer Science, Engineering, Mathematics, or a related field, 

·       Overall 12 + years in IT delivery including 3+ years of hands-on experience in AI/ML and data engineering, with a strong foundation in enterprise-scale solution development

·       Strong hands-on expertise in Python, Apache Spark, PySpark, and Databricks for large-scale data processing and AI model development

·       Proven ability to design, deploy, and evaluate machine learning and generative AI models using platforms such as OpenAI, Hug