Data Architect

TEKsystems•Lake Forest, IL

2d•$90 - $90•Hybrid

About The Position

Looking for Senior Data & Graph Technology Engineers who can rapidly onboard and begin building an enterprise‑grade Knowledge Graph + AI/LLM–enabled platform that supports Market Access field teams and downstream Commercial operations. These resources will be instrumental in delivering an MVP by June 30, powering a new solution that integrates: - A Neo4j‑based knowledge graph - A chatbot/LLM interaction layer - Patient Access + Market Access compliant data - Backend services, metadata systems, and logging frameworks - An interface for Market Access field teams to access graph‑derived insights AbbVie wants high‑caliber, hands‑on Senior technologists who can immediately contribute, explore data with minimal hand‑holding, and deliver rapid progress in an agile, iterative manner.

Requirements

Knowledge Graph
data architecture
Graph Architecture
Neo4j
Ontology Design
Data Modeling
ML Architecture
AI Architecture
LLMs
Generative AI Integration
python

Nice To Haves

AWS ecosystem experience (DynamoDB, Lambda, S3, API Gateway, etc.)
Prior experience working with Market and/or Patient Access data is a major advantage
Prior experience contributing to ChatGPT/LLM-based enterprise pilots or prototypes (preferred)
Ability to thrive in fast-paced MVP cycles; comfortable with ambiguity and rapid iteration

Responsibilities

Architecture Ownership & Solution Design:
Own and lead end-to-end technical architecture and solution design (both high-level and low-level) for a Knowledge Graph–based platform using Neo4j.
Define, document, and enforce architecture principles, graph data modeling standards, ontology design patterns, and best practices to ensure scalability, performance, maintainability, and extensibility.
Translate complex Market Access, Patient Access, and commercial use cases into robust, graph-based technical designs.
Knowledge Graph & Ontology Design:
Design and implement the graph data model and ontology in Neo4j, including nodes, relationships, properties, constraints, and indexes.
Lead ontology creation, evolution, and governance, ensuring alignment with business requirements and data realities.
Ensure the Knowledge Graph supports AI/ML and LLM-driven use cases, not just querying or visualization.
Architecture Artifacts & Documentation:
Create clear, reusable, and concise architecture artifacts, including:
Graph data models and ontology diagrams
Solution architecture diagrams and flowcharts
Interface specifications and API contracts
Data mappings and integration designs
Maintain documentation that is implementation-ready, minimizing ambiguity and follow-up questions from development teams.
AI/ML & Platform Integration:
Guide the integration of AI/ML models, LLMs, and chatbot interfaces with the Knowledge Graph.
Define how AI components consume and enrich graph-based knowledge to produce accurate, explainable outputs.
Partner with development teams to ensure AI-driven workflows are grounded in structured graph data and meet enterprise standards.
Data Integration & Engineering Oversight:
Oversee data integration strategy, including the design of ETL/ELT pipelines where applicable.
Ensure all ingestion pipelines conform to graph modeling standards, data quality rules, and compliance requirements.
Provide architectural guidance for logging, metadata, and observability components (e.g., DynamoDB or other AWS services).
Technical Leadership & Governance:
Serve as the primary technical authority and point of contact between AbbVie BTS and the SI partner’s development team.
Conduct technical walkthroughs, design reviews, and architecture presentations with stakeholders to ensure alignment and clarity.
Lead technical reviews and sign-offs on solution components and code, ensuring adherence to approved architecture and standards.
Drive adoption of reusable components, utilities, and configuration to improve implementation efficiency and consistency.
Hands-On Support & Delivery Enablement:
Provide hands-on technical support during development, testing, and defect resolution.
Troubleshoot complex technical issues and propose practical, scalable solutions.
Work closely with SI partner teams to proactively identify risks, validate implementation approaches, and mitigate technical challenges.
Knowledge Transfer & Continuous Improvement:
Ensure all technical documentation is complete, current, and easily consumable by both SI and internal teams.
Facilitate knowledge transfer and onboarding for SI partner teams and AbbVie BTS as needed.
Stay current with Neo4j capabilities, graph technology trends, and AI best practices, recommending improvements and optimizations where appropriate.