Senior Specialist

• Design and implement scalable enterprise graph data models and graph-based architectures.
• Build and maintain graph platforms using Neo4j, Amazon Neptune, Dydra, or equivalent technologies.
• Develop graph schemas, ontologies, semantic models, and metadata frameworks.
• Design and optimize ETL/ELT pipelines for integrating data from diverse enterprise sources.
• Develop graph queries, APIs, and data services using Cypher, SPARQL, Gremlin, and Python.
• Build data products supporting analytics, AI/ML, semantic search, RAG, and knowledge discovery.
• Ensure data quality, governance, security, and performance across graph and analytical platforms.
• Lead architecture discussions, implement DevOps practices, mentor engineering teams, and drive technical excellence.

Required Skills

• Enterprise Graph Data Modeling
• Neo4j / Graph Databases
• ANSI-SQL
• Property Graphs (LPG), RDF, Ontologies, and Semantic Modeling
• Graph Query Languages: Cypher, SPARQL, and Gremlin
• Python Development
• ETL/ELT Development
• Apache Spark, Databricks, and Airflow
• Data Integration and Data Engineering
• Relational, Analytical, and Lakehouse Data Modeling
• Distributed Data Platforms
• DevOps, CI/CD, and Infrastructure as Code
• Enterprise Data Architecture

Preferred / Good to Have

• Databricks Lakehouse Platform, Delta Lake, and Unity Catalog.
• AWS services: S3, Neptune, Glue, Athena, IAM, and Lambda.
• Graph Analytics and Graph Data Science.
• Graph-based Machine Learning and AI Solutions.
• Semantic Search, Knowledge Graphs, and RAG Solutions.
• Recommendation Systems and Recommendation Engines.
• Data Visualization tools: Dash, Shiny, Grafana, Splunk, and Databricks Visualization.
• Semantic Modeling Frameworks and Enterprise Metadata Management.
  • Graph Data Science Libraries.