Semantic Modeler/AI Engineer

ONLY OPEN TO W2 EMPLOYEES



Role: Semantic Modeler/ AI Engineer

Must Have Technical/Functional Skills

We are seeking a skilled AI Data Engineer and/or Semantic Modeler to join our dynamic team. The ideal candidate will be responsible for designing, developing, and maintaining metric store and semantic models that support AI and machine learning initiatives. This role requires a strong understanding of data architecture, data modeling, and AI technologies.

Technical Skills:

· AI technologies such as dbt metricflow, neo4j knowledge graph, AtScale, AWS Quicksight, AWS Neptune
· Expertise in SQL and Data Modeling and experience with relational databases (e.g., Redshift, RDS, Databricks)

Roles & Responsibilities

· Define data model specific to business requirements
· Standardize metrics and KPIs
· Setup metric store tool such as DBT metricflow / DBT Semantic Layer tool
· Map semantic model to data layer
· Design, develop and fine tune models based on business requirements
· Execute Gen AI use cases wherever applicable
· Maintain MLOps for the automation and monitoring of ML pipelines
· Stay updated with the latest trends in AI, data engineering, and semantic technologies · Must have experience in AI technologies such as dbt metricflow, neo4j knowledge graph, AtScale, AWS Quicksight, AWS Neptune

Pre-Screening Questionnaire

· What is your understanding of semantic layer for data lakehouse and / or Data warehouse environment?(Please

provide examples of how you have applied them.)

· Have you created or worked with ontologies? If yes, please describe your experience.

· What is your experience with DBT? (Please describe projects where you have used DBT, including the complexity

and scale.)

· What are the key components of a DBT project? (e.g., models, seeds, snapshots, etc.)

· How do you define and manage metrics in MetricFlow?

· Can you explain the core components of a knowledge graph? (e.g., entities, relationships, attributes)

· What technologies or frameworks have you used to build or manage knowledge graphs?(e.g., Neo4j, RDF, OWL,

GraphQL)

· How do you model entities and relationships in a knowledge graph? (Please provide examples of your

approach.)

· What methods do you use to query knowledge graphs? (e.g., SPARQL, Cypher)