Data Architect / Engineer - Assistant Vice President

Who we are looking for

We are looking for an experienced Data Architect / Data Engineer, Assistant Vice President to provide technical and architectural leadership for enterprise cybersecurity data, analytics, and AI capabilities.

This role will define and deliver scalable data architectures, Lakehouse platforms, data models, integration frameworks, and AI-ready data foundations. The individual will partner with cybersecurity, engineering, architecture, governance, risk, and product teams to transform complex security data into scalable, governed, and actionable solutions.

What we value

These skills will help you succeed in this role:

· Strong expertise in Data Architecture, Data Engineering, Cloud Data Platforms, and AI/GenAI.

· Deep understanding of Lakehouse architecture, distributed processing, ETL/ELT, data modeling, integration, data quality, metadata, lineage, and governance.

· Strong hands-on skills in Python, SQL, PySpark, Databricks, Delta Lake, and cloud technologies.

· Experience designing enterprise-scale, secure, resilient, and governed data platforms.

· Practical knowledge of LLMs, RAG, embeddings, vector/semantic search, NLP, knowledge graphs, and responsible AI architecture.

· Understanding of cybersecurity telemetry, SIEM/SOAR, vulnerability management, identity risk, threat intelligence, and incident data.

· Ability to influence architecture decisions and engage effectively with technical teams, architects, product leaders, and senior stakeholders

About State Street

State Street is one of the world's leading providers of financial services to institutional investors, including investment servicing, investment management, investment research, and trading. Technology and cybersecurity are critical enablers of our ability to safeguard information, support clients, and operate at enterprise scale.

We value collaboration, innovation, inclusion, and responsible technology. We are looking for technical talent who can solve meaningful problems, communicate clearly, and help build secure, scalable, data-driven capabilities for the future.

State Street is an equal opportunity and affirmative action employer.

About State Street

Across the globe, institutional investors rely on us to help them manage risk, respond to challenges, and drive performance and profitability. We keep our clients at the heart of everything we do, and smart, engaged employees are essential to our continued success.

We are committed to fostering an environment where every employee feels valued and empowered to reach their full potential. As an essential partner in our shared success, you’ll benefit from inclusive development opportunities, flexible work-life support, paid volunteer days, and vibrant employee networks that keep you connected to what matters most. Join us in shaping the future.

As an Equal Opportunity Employer, we consider all qualified applicants for all positions without regard to race, creed, color, religion, national origin, ancestry, ethnicity, age, disability, genetic information, sex, sexual orientation, gender identity or expression, citizenship, marital status, domestic partnership or civil union status, familial status, military and veteran status, and other characteristics protected by applicable law.

Discover more information on jobs at StateStreet.com/careers

Read our CEO Statement

What you will be responsible for

As a Data Architect / Engineer, you will:

· Architect and deliver scalable Lakehouse and enterprise data platforms, pipelines, data models, and reusable data products.

· Define target-state data architecture, reference architectures, integration patterns, standards, and technical roadmaps.

· Design solutions for large-scale structured, semi-structured, time-series, graph, and unstructured cybersecurity data.

· Establish reusable frameworks for ETL/ELT, ingestion, orchestration, data quality, metadata, lineage, observability, and reconciliation.

· Establish architecture standards for scalability, resiliency, performance, security, privacy, governance, and auditability.

· Design AI-ready data architectures supporting LLMs, RAG, embeddings, vector/semantic search, NLP, knowledge graphs, and intelligent workflows.

· Integrate data across cloud platforms, enterprise systems, cybersecurity platforms, and SIEM/SOAR ecosystems.

· Provide technical leadership through architecture reviews, engineering standards, technology evaluation, and solution governance.

· Partner with stakeholders to translate cybersecurity and business requirements into scalable data and AI solutions.

· Mentor engineering teams and drive reusable architecture patterns, automation, and engineering best practices.

Minimum Qualifications

· 15+ years of experience across Data Architecture, Data Engineering, enterprise data platforms, cloud, analytics, AI, or related technology disciplines.

· Bachelor's degree in Computer Science, Engineering, Information Systems, Cybersecurity, or a related discipline, or equivalent experience.

· Proven experience architecting and delivering enterprise-scale data platforms and Lakehouse solutions.

· Strong experience with Python, SQL, PySpark, Databricks, ETL/ELT, distributed processing, data modeling, and AWS or comparable cloud platforms.

· Experience with CI/CD, automated testing, monitoring, data governance, and engineering best practices.

· Experience in cybersecurity, financial services, regulated industries, or large enterprise environments is highly desirable

· Strong written, verbal, analytical, and stakeholder communication skills.

Additional Requirements

· This is an individual contributor role with strong cross-functional collaboration expectations.

· The role will require sound judgment when working with sensitive cybersecurity, risk, operational, and regulated data.

· The candidate should be comfortable balancing exploratory data science, production-minded analytical delivery, and stakeholder communication.

Technical Skills

Skill Area

Expected Capabilities

Data Architecture

Lakehouse, enterprise data modeling, data domains, data products, data contracts, integration patterns, reference architecture, metadata and governance

Data Engineering

Python, SQL, PySpark, Databricks, Delta Lake, ETL/ELT, orchestration, data quality, lineage and observability.

AI & Gen AI

LLMs, RAG, embeddings, vector/semantic search, NLP, knowledge graphs, responsible AI and AI-enabled workflows.

Cloud & DevOps

AWS, CI/CD, Git, automated testing and scalable cloud data processing

Cybersecurity Data

SIEM/SOAR, security telemetry, vulnerability, identity, incident and threat intelligence data.

Visualization & BI

Power BI, semantic models, operational metrics and executive reporting.