Enterprise AI-Ready Data Architect

Enterprise AI-Ready Data Architect

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

• 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
• 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.
• 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
• 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.
• 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