Senior Data Engineer
7 days ago
East Hanover
Job Title: Enterprise AI-Ready Data Architect / Senior Data Engineer Duration : 6 Months Location: East Hanover (Onsite: 3days & 2 days remote a week) 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