EI
Enterprise AI-Ready Data Architect
eTeam Inc.
🇺🇸 United States
Hybrid
Senior
6 months ago
$53.57 – $64.28 / hour
- AI
- Machine Learning
- Fabric
- Domain-Driven Design
- Vector Search
- RAG
- ETL
- ELT
- Data Modeling
- SQL
- dbt
- Airflow
- Snowflake
- Databricks
- Collibra
- Salesforce
- Power BI
- AWS
- Azure
- GCP
- Neo4j
- Palantir
- TOGAF
- Data Architecture
- AI/ML
6 months ago
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 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.
• 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 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
• 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
• 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
Enterprise AI-Ready Data Architect · eTeam Inc.