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Enterprise AI-Ready Data Architect
Job Location:
Job Location:
East Hanover, NJ - USA
Hourly Salary:
USD 53 - 64
Hourly Salary:
Posted:
8 September 2026 (3 days ago)
Posted:
Application Deadline:
6 December 2026
Application Deadline:
Vacancies:
1 Vacancy
Vacancies:
Job Summary
Job Title: Enterprise AI-Ready Data Architect
Location: East Hanover (Onsite: 3days & 2 days remote a week)
Duration: 06 Months
Pay Range: $(53.57 $64.28)/hr on W2 all-inclusive without benefits
Job Description:
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 stakeholdersin pharmaceutical domains.
Key Responsibilities
1) Enterprise Data Architecture (AI-Ready by Design)
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
2) Data Products Semantic Products and Master Data
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.
3) AI Integration and GenAI Enablement
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
4) Data Engineering Delivery
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.
5) Governance and Standards (Embedded)
Implement open-source data standards across:
Data contracts
Data quality
Data lineage
Lead metadata-driven governance through metadata management observability and policy-aligned design.
Skills and Qualifications
Core Technical Skills
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
Domain and Modeling Expertise
Experience with data modeling techniques:
Conceptual logical physical modelingpreferably for the pharmaceutical industry
Semantic modeling ontology design and reusable metric layers
MDM concepts and implementation approaches
AI and GenAI Enablement Skills
Familiarity with GenAI technologies for enhancing analysis/reporting and data enrichment
Experience with embeddings vector search RAG patterns and entity resolution/linking concepts
Nice to Have
Experience with Palantir platform
Recommended Certifications
CDMP (DAMA)
TOGAF
EDM Council frameworks:
DCAM CDMC Open Knowledge Graph Data Ethics and Responsible AI
Qualifications
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
Bachelors 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
Location: East Hanover (Onsite: 3days & 2 days remote a week)
Duration: 06 Months
Pay Range: $(53.57 $64.28)/hr on W2 all-inclusive without benefits
Job Description:
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 stakeholdersin pharmaceutical domains.
Key Responsibilities
1) Enterprise Data Architecture (AI-Ready by Design)
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
2) Data Products Semantic Products and Master Data
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.
3) AI Integration and GenAI Enablement
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
4) Data Engineering Delivery
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.
5) Governance and Standards (Embedded)
Implement open-source data standards across:
Data contracts
Data quality
Data lineage
Lead metadata-driven governance through metadata management observability and policy-aligned design.
Skills and Qualifications
Core Technical Skills
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
Domain and Modeling Expertise
Experience with data modeling techniques:
Conceptual logical physical modelingpreferably for the pharmaceutical industry
Semantic modeling ontology design and reusable metric layers
MDM concepts and implementation approaches
AI and GenAI Enablement Skills
Familiarity with GenAI technologies for enhancing analysis/reporting and data enrichment
Experience with embeddings vector search RAG patterns and entity resolution/linking concepts
Nice to Have
Experience with Palantir platform
Recommended Certifications
CDMP (DAMA)
TOGAF
EDM Council frameworks:
DCAM CDMC Open Knowledge Graph Data Ethics and Responsible AI
Qualifications
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
Bachelors 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
Required Experience:
Staff IC
