Enterprise AI-Ready Data Architect

Novartis Pharmaceuticals Corporation•East Hanover, NJ

1d•$54 - $64•Hybrid

About The Position

The Enterprise AI-Ready Data Architect / Senior Data Engineer is a hybrid role with a focus on enterprise data architecture, AI integration, and hands-on data engineering. You will design and implement AI-ready, analytics-ready data products and semantic layers (including ontologies) that enable scalable enterprise analytics and integration with AI agents and GenAI use cases. You will embed governance-by-design (quality, lineage, contracts, observability) and partner closely with business and technology stakeholders—in pharmaceutical domains.

Requirements

Advanced SQL proficiency
Data platforms and governance tooling experience (one or more): Snowflake, Databricks, Collibra, Salesforce
ELT/ETL and orchestration: dbt, Airflow, Dataiku
BI and reporting: Power BI
Cloud platforms: AWS, Azure, GCP
Modern architecture and data management: Data Mesh, Data Fabric, streaming, metadata-driven architecture
Graph and semantic technologies: Knowledge graphs, property graphs (Neo4J), RDF/OWL, SPARQL, graph query languages
Experience with data modeling techniques: Conceptual, logical, physical modeling—preferably for the pharmaceutical industry
Semantic modeling, ontology design, and reusable metric layers
MDM concepts and implementation approaches
Familiarity with GenAI technologies for enhancing analysis/reporting and data enrichment
Experience with embeddings, vector search, RAG patterns, and entity resolution/linking concepts
10+ years of experience in data architecture, process automation, implementation and large-scale data engineering, ideally in pharmaceutical
Advanced technical engineering and hands-on experience in data modeling for OLAP, workflow automation, AI/ML integration
ETL pipeline design and development
Bachelor’s degree in computer science, information technology, engineering, or data science
Strong problem-solving skills and attention to detail.
Excellent communication skills with the ability to work with senior stakeholders to translate business requirements to technical data requirements

Nice To Haves

Experience with Palantir platform

Responsibilities

Define and deliver strategic enterprise data architectures that scale and support AI-ready outcomes.
Design data workflows capturing as-is and to-be states for enterprise modernization.
Establish architecture patterns for: Semantic Context Layer, Data Warehouses, Data Lakehouses, Data Catalogs and Data Marketplaces, Event-driven and metadata-driven architectures, Distributed data management (Data Mesh, Data Fabric, Domain-Driven Design), Streaming data management.
Design data products that are AI-ready and reusable across domains and use cases.
Build and govern semantic models, metrics-first modeling, and ontologies (knowledge graph concepts).
Deliver Master Data Management (MDM) capabilities and align master/reference data with business needs.
Support structured and unstructured data management to enable broader AI and analytics capabilities.
Enable contextual intelligence and data enrichment using: Contextual retrieval, entity linking, enrichment using LLMs and embeddings, Vector search, RAG pipelines, and LLM-based enrichment.
Implement graph-based approaches: RDF, OWL, and SPARQL querying, Property graph / knowledge graph modeling for relationships and reasoning.
Design and implement robust ETL/ELT pipelines and orchestration frameworks.
Develop high-quality transformations and data modeling using: Advanced SQL, Tools such as dbt, Airflow, Dataiku.
Ensure production-grade engineering practices for performance, reliability, and maintainability across pipelines.
Implement open-source data standards across: Data contracts, Data quality, Data lineage.
Lead metadata-driven governance through metadata management, observability, and policy-aligned design.