Enterprise AI-Ready Data Architect

eTeam; East Hanover (NJ); On-site; USD 73,012 - 82,656; Full time

Job summary

A leading technology consulting firm based in East Hanover, NJ, is seeking an experienced Enterprise AI-Ready Data Architect. The role combines enterprise data architecture and AI integration, requiring a strong background in data engineering and governance in pharmaceutical domains. Candidates should have over 10 years of experience in data architecture and be skilled in advanced SQL and data modeling techniques. A hybrid work model is offered with flexibility in work location.

Qualifications

• 10+ years of experience in data architecture, ideally in pharmaceutical.
• Strong hands-on experience in data modeling for OLAP and workflow automation.
• Excellent communication skills with senior stakeholders.

Responsibilities

• Define strategic enterprise data architectures that support AI‑ready outcomes.
• Design data workflows for enterprise modernization.
• Implement contextual intelligence using LLMs and embeddings.

Skills

Advanced SQL proficiency

Data platforms experience (Snowflake, Databricks, etc.)

Knowledge graphs and graph query languages

Experience with data modeling techniques

Familiarity with GenAI technologies

Education

Bachelor’s degree in computer science or related field

Tools

Airflow

Dataiku

Job description

Job Title: Enterprise AI-Ready Data Architect

Location: East Hanover, NJ

Duration: 06 Months

Pay Range: $(53.00 – $60.00)/hr on W2 all-inclusive without benefits

Hybrid: 3 days onsite; 2 days remote

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 stakeholders in 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.
• Semantic Context Layer
• Data Warehouses, Data Lakehouses
• Data Catalogs and Data Marketplaces
• Event‑driven and metadata‑driven architectures

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
• Design and implement robust ETL/ELT pipelines and orchestration frameworks.
• Develop high‑quality transformations and data modeling using:
• Tools such as dbt, Airflow, Dataiku
• Ensure production‑grade engineering practices for performance, reliability, and maintainability across pipelines.

5) Governance and Standards (Embedded)

• 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:
• BI and reporting:
• Modern architecture and data management:
• 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 modeling—preferably 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

• 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
• 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