Market Access Data Architect

TEKsystems•Berwyn, IL

1d•$90 - $90•Hybrid

About The Position

Data Engineering & Integration Coordinate and implement Specialty Pharmacy and Market Access data integration solutions in partnership with Commercial Data Lake teams. Develop ETL/ELT pipelines using PySpark and Python to ingest, transform, aggregate, and orchestrate data for end-user consumption. Build competency across Market Access and Patient Services datasets, including: Rebate data EDI sales and chargeback data Master data Copay and affordability data Medical and prescription claims Care model and patient services data Apply best practices for data quality monitoring, validation, and reporting. Leverage big data tools and architectures (e.g., Spark, Hive, Impala, cloud data platforms) to answer critical business questions. Knowledge Graph Architecture & Development Architect, design, and build Neo4j-based knowledge graph structures supporting Market Access and Patient Services use cases. Ingest, model, and connect complex pharma datasets including patients, coverage, contracts, benefits, services, and gross-to-net (GTN) components. Design and optimize graph schemas, nodes, relationships, metadata layers, indexing strategies, and query performance. Ensure graph data is accurate, traceable, and aligned with enterprise data governance and compliance standards. AI / LLM Integration Design and integrate LLM-powered chatbot and assistant workflows on top of the knowledge graph. Implement prompt engineering, retrieval-augmented generation (RAG), and domain-specific grounding using graph and document sources. Ensure AI components follow enterprise standards for explainability, auditability, and compliance. Collaborate with enterprise AI teams to align with approved frameworks, guardrails, and tooling. Backend Services, Metadata & Logging Build backend services that interface with the knowledge graph, LLM systems, and field-facing applications. Implement robust metadata, logging, and monitoring frameworks to support auditability and regulated environments. Utilize cloud-native services (e.g., object storage, NoSQL stores, serverless compute, APIs) where appropriate. Agile Delivery & Stakeholder Collaboration Deliver at high velocity in an agile, iterative environment with visible daily progress. Participate in standups, technical design reviews, and sprint planning. Own work end-to-end: design, implementation, testing, and validation. Proactively identify data gaps, design risks, and integration issues before they become blockers. Establish and maintain strong working relationships across technical teams and external partners, managing expectations and communication effectively.

Requirements

Expert-level experience with: Data architecture and data modeling Graph modeling and Neo4j Python and PySpark AI/LLMs and chatbot architectures Cloud platforms (AWS preferred)
Strong hands-on experience integrating complex enterprise datasets.
Proven ability to work independently with minimal direction.
Experience delivering MVPs or prototypes under tight timelines.
Experience working in highly regulated environments (data privacy, audit, compliance).

Nice To Haves

Prior experience with Market Access, Patient Services, or Specialty Pharmacy data
Experience contributing to enterprise LLM pilots or production AI solutions
Familiarity with AWS services such as DynamoDB, Lambda, S3, API Gateway
Experience designing AI-driven insights layers on top of enterprise data platforms

Responsibilities

Coordinate and implement Specialty Pharmacy and Market Access data integration solutions in partnership with Commercial Data Lake teams.
Develop ETL/ELT pipelines using PySpark and Python to ingest, transform, aggregate, and orchestrate data for end-user consumption.
Build competency across Market Access and Patient Services datasets, including: Rebate data EDI sales and chargeback data Master data Copay and affordability data Medical and prescription claims Care model and patient services data
Apply best practices for data quality monitoring, validation, and reporting.
Leverage big data tools and architectures (e.g., Spark, Hive, Impala, cloud data platforms) to answer critical business questions.
Architect, design, and build Neo4j-based knowledge graph structures supporting Market Access and Patient Services use cases.
Ingest, model, and connect complex pharma datasets including patients, coverage, contracts, benefits, services, and gross-to-net (GTN) components.
Design and optimize graph schemas, nodes, relationships, metadata layers, indexing strategies, and query performance.
Ensure graph data is accurate, traceable, and aligned with enterprise data governance and compliance standards.
Design and integrate LLM-powered chatbot and assistant workflows on top of the knowledge graph.
Implement prompt engineering, retrieval-augmented generation (RAG), and domain-specific grounding using graph and document sources.
Ensure AI components follow enterprise standards for explainability, auditability, and compliance.
Collaborate with enterprise AI teams to align with approved frameworks, guardrails, and tooling.
Build backend services that interface with the knowledge graph, LLM systems, and field-facing applications.
Implement robust metadata, logging, and monitoring frameworks to support auditability and regulated environments.
Utilize cloud-native services (e.g., object storage, NoSQL stores, serverless compute, APIs) where appropriate.
Deliver at high velocity in an agile, iterative environment with visible daily progress.
Participate in standups, technical design reviews, and sprint planning.
Own work end-to-end: design, implementation, testing, and validation.
Proactively identify data gaps, design risks, and integration issues before they become blockers.
Establish and maintain strong working relationships across technical teams and external partners, managing expectations and communication effectively.