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 Responsibilities1) 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 DataDesign 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 EnablementEnable 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 QualificationsCore Technical SkillsAdvanced SQL proficiency Data platforms and governance tooling experience (one or more):Snowflake Databricks Collibra SalesforceELT/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 languagesDomain and Modeling ExpertiseExperience 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 SkillsFamiliarity with GenAI technologies for enhancing analysis/reporting and data enrichment Experience with embeddings, vector search, RAG patterns, and entity resolution/linking concepts Nice to HaveExperience with Palantir platform Recommended CertificationsDCAM, 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