AI Data & Knowledge Engineer - Assistant Vice President
AI in this role
Lead the design and evolution of enterprise-grade data and knowledge foundations for AI systems including RAG and knowledge graphs.
AI Data & Knowledge Engineer - AVP
About the Role
We are looking for a senior AI Data & Knowledge Engineer to lead the design and evolution of enterprise-grade data and knowledge foundations for AI systems. This role requires deep expertise in data engineering, big data architecture, ETL/ELT design, and scalable backend pipeline development, alongside strong experience in RAG, GraphRAG, ontologies, and knowledge graph technologies. The role will define standards and architecture patterns that ensure AI applications are powered by reliable, governed, and scalable data platforms.
Key Responsibilities
- Lead architecture and technical design for AI-ready data platforms, including ingestion, transformation, storage, serving, and retrieval layers.
- Define standards for scalable batch and streaming data pipelines, ETL/ELT frameworks, and backend data services supporting AI applications.
- Own data and knowledge architecture across the platform, including knowledge graphs, ontologies, semantic models, and retrieval architectures.
- Establish and enforce standards for data contracts, lineage, observability, governance, and quality controls for AI systems.
- Partner with platform, architecture, and AI engineering teams to design robust integration patterns across enterprise source systems and AI services.
- Drive adoption of modern data engineering practices, including orchestration, CI/CD for data pipelines, reusable framework components, and automated quality checks.
- Lead evaluation and implementation of technologies across big data processing, vector search, graph stores, metadata tooling, and AI data infrastructure.
- Coach and develop Officers and Senior Associates; support hiring and capability building across the pod.
- Represent the Data/Knowledge function in architecture reviews, governance forums, and cross-functional planning discussions.
Required Qualifications
- Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related field.
- 8–14 years of experience in data engineering, data architecture, platform engineering, or large-scale data systems.
- Deep expertise in building scalable data pipelines, ETL/ELT frameworks, and backend data platforms.
- Strong experience with distributed data processing technologies such as Spark, Databricks, Flink, or equivalent ecosystems.
- Experience designing enterprise data architectures across data lakes, warehouses, lakehouses, and AI-serving layers.
- Strong understanding of orchestration, observability, performance tuning, reliability engineering, and cost-aware pipeline design.
- Deep expertise in RAG, GraphRAG, knowledge graphs, ontology design, semantic modeling, and metadata-driven architectures.
- Experience owning data governance frameworks, data contracts, lineage, and compliance controls at scale.
- Cloud certifications in AWS and/or Azure are a plus.
- Proven experience leading technical teams and influencing architecture decisions across multiple domains.
- Strong written and verbal communication skills, including the ability to present technical designs and trade-offs clearly.
Work Schedule
On-premise
Keywords
Data Engineering, Big Data Architecture, ETL, ELT, Data Pipelines, Spark, Databricks, Kafka, Airflow, Lakehouse, Data Governance, Data Contracts, Metadata, Lineage, RAG, GraphRAG, Knowledge Graphs, Ontology, Vector Database, AI Data Platform
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.
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How we rate this
AI Data & Knowledge Engineer - Assistant Vice President at State Street rates 85 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Open the description and count.
Prepare for this job
A free preview built only from this posting: what it asks for, what you could be asked in an interview, and how to adjust your resume.
Skills and AI tools this role asks for
Questions you could be asked
- How would you design a retrieval step so the model answers from real data instead of guessing?
- Tell me about a project where data engineering was part of your work. What did you do?
- Tell me about a project where knowledge graphs was part of your work. What did you do?
- Tell me about a project where etl was part of your work. What did you do?
- Tell me about a project where graphrag was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: RAG, Data Engineering, Knowledge Graphs, ETL, and Graphrag. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them — a tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped — this role is judged on the AI system itself, not the tools around it.
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