Description & Requirements The Director of Data & Intelligence serves as a senior leader within the Analytics and Applied Intelligence (AAI) practice and is accountable for the Data Foundation layer (Layer 1) of the firm's agentic intelligence framework, including the semantic fabric, vector stores, knowledge graphs, MCP connectors, and Zero-ETL pipelines that power autonomous systems and Client Zero solutions. This leader provides strategic direction, mentorship, technical coaching, and people leadership to the firm's Data & Intelligence Engineers, including professionals transitioning from Backend Data Designer and Data Engineer roles. Operating as a peer and key collaborator to the senior leaders of the AI Solutions Architects and Intelligence Designers teams, this role partners closely on solution architecture, end-to-end agentic delivery, and the development of Center of Excellence standards. The position follows a flexible hybrid work model with approximately 10% travel and requires availability during Eastern Standard Time (EST) business hours. Due to project requirements, all professionals serving in this role must reside and perform all work within the United States. Candidates must be legally authorized to work in the United States now and in the future. Verification of employment eligibility will be required at the time of hire, and visa sponsorship is not available for this position.
What You Will Do: - Lead the Data & Intelligence Engineering discipline across the AAI practice - setting technical direction, quality standards, and delivery patterns for the Layer 1 Data Foundation that supports delivery.
- Provide coaching, career development, and performance feedback to Data & Intelligence Engineers.
- Partner with peer leaders of the AI Solutions Architects and Intelligence Designers to define solution architecture, hand-offs across the three logic layers (Data, Reasoning, Validation), and shared standards for MCP, evals, and HITL.
- Contribute to the AAI Center of Excellence (Hub) - selecting frontier tooling, codifying standards (Zero-ETL, vector DBs, knowledge graphs, MCP), and ensuring cross-team knowledge sharing.
- Represent the practice on AI Ethics, model governance, data sovereignty, and responsible AI guardrails, in alignment with the AAI Steering Committee and Client Zero principles.
- Architect and oversee the Data Foundation layer of agentic systems: vector stores (Pinecone, Weaviate, Milvus), knowledge graphs (Neo4j), semantic layers, and Zero-ETL / real-time streaming pipelines.
- Thorough understanding of data modeling, relational, and non-relational principles; expert level SQL
- Expertise in Python data engineering packages and patterns, including Python and PySpark
- Familiarity and experience with the different data engineering platforms commonly used such as Fabric, Databricks, Snowflake, Synapse
- Solid leadership-level fluency with cloud fundamentals (Azure preferred, GCP, AWS) and modern data architecture (lakehouse, data contracts, governance, lineage)
- Expected to actively collaborate with clients and team members to meet project objectives and timelines; Strong communication skills (verbal and written) for executive, client, and team audiences.
- Experience with building, testing and deploying data pipelines, ML/AI models; understanding of GenAI/Agentic AI use cases (experience strongly preferred)
- Design and govern MCP (Model Context Protocol) connectors that securely expose enterprise sources (Jira, CRM, ERP, lakehouse) as tools consumable by agents and frontier models.
- Set standards for Graph-RAG, RAG optimization, chunking strategy, and retrieval evaluation to minimize hallucinations and ensure high-quality context for downstream LLM components.
- Partner with AI Solutions Architects to ensure data tools, semantic layers, and MCP services meet the needs of agentic workflows (LangChain, LangGraph, CrewAI) and frontier model integrations (GPT, Claude, Gemini).
- Partner with Intelligence Designers to ensure data structures support HITL workflows, Action Centers, evals, and Golden Set testing.
- Enforce row-/column-level security, federated identity (Entra ID / OAuth), and policy-as-code guardrails across all agent-accessible data.
Minimum Qualifications: - BS in data science, computer science or other related fields (MS strongly preferred)
- 8+ years of relevant data engineering / data science experience, with demonstrated depth in modern data architecture (Zero-ETL, vector/graph databases, semantic layers, RAG)
- 7+ years of experience leading, coaching, and developing teams of professionals (typically 5-10+), including senior individual contributors; consulting environment strongly preferred.
- Proven track record partnering with peer technical leaders (e.g., solutions architecture, UX/interface design) to deliver integrated, multidisciplinary platforms.
- Demonstrated experience deploying RAG architectures and MCP-based integrations in production environments, including evaluation frameworks (evals, RAGAS, Golden Set testing) for probabilistic systems.
- Familiarity with AI Ethics, responsible AI guardrails, and governance frameworks suitable for enterprise and regulated environments.
Preferred Qualifications: - Prior experience in professional services or consulting, ideally leading a data engineering or applied AI practice area.
- Experience translating client business problems into agentic data architectures (semantic layers, knowledge graphs, MCP tool design).
- Hands-on exposure to LLM orchestration frameworks (LangChain, LangGraph, CrewAI) sufficient to coach engineers on integration patterns.
- Experience with production agent monitoring, observability, SLA design, and incident response for probabilistic systems (in partnership with the Agent Operations Lead).
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