AI Engineer /Lead
Summary
Build and own a production-grade semantic layer and graph-powered metrics store for AI-driven analytics and BI, using Cube.js/dbt and Neo4j/GraphQL.
Our client is building a next-generation data product platform where analytics is not an afterthought - it IS the product. We are looking for an Analytics Engineer who has built a production-grade Semantic Layer using Cube.js or dbt Semantic flow and has strong experience working with Graph.
You will own the metrics store + graph layer that powers BI, and AI-driven experiences for thousands of users.
Must-Have Skills (Non-Negotiable)
1. Semantic Layer Expertise - Must have ONE:
- Track A - Cube.js / CubeCore: 2+ years in production building cubes, views, pre-aggregations, rollups, blending, securityContext, multi-tenancy, and Cube Store. Experience with Cube Cloud deployment on Docker/K8s.
- OR Track B - dbt Semantic flow: 2+ years in production building semantic models, metrics (simple/derived/cumulative/conversion), saved queries, and exposing via GraphQL/JDBC APIs. Experience with dbt Cloud/Core.
2. Graph Expertise - Must Have:
- Hands‑on experience in Graph data modeling and implementation.
- Proficiency in at least one: Neo4j / Amazon Neptune / TigerGraph / Memgraph OR GraphQL API architecture
- Strong knowledge of Graph query languages: Cypher / Gremlin / GraphQL
- Experience building Knowledge Graphs, Metrics Graphs, or Property Graphs for analytics use cases
- Understanding of how to integrate graph context with semantic/metrics layer
3. Core Analytics Engineering:
- Expert‑level SQL and Dimensional Data Modeling (Star, Snowflake, Data Vault)
- Strong hands‑on with Modern Data Warehouse: Snowflake / BigQuery / Databricks / Redshift
- Expert in dbt for transformation
- Experience building Row‑Level Security, performance optimization, and caching strategies for sub‑second analytics
- Experience powering BI tools or customer‑facing embedded analytics (Superset) (Metabase) (Looker) (PowerBI)
Key Responsibilities:
- Design, build and own the end‑to‑end Semantic Layer - the single source of truth for all business metrics.
- Architect and build Graph‑based models (Knowledge Graph) to add context, relationships, and lineage to metrics.
- Build secure, high‑performance APIs (GraphQL/REST) for internal and embedded analytics consumption.
- Own pre‑aggregation strategy, caching, and query performance tuning.
- Implement enterprise‑grade governance, security, and multi‑tenant access control.
- Partner with Data Engineering, Product, and Frontend teams to deliver self‑serve data products.
- Own documentation, data quality, and adoption of the semantic layer across the organization.
Tech Stack:
Cube.js, dbt, Snowflake/BigQuery, Neo4j/GraphQL, Airflow, Kubernetes, TypeScript/Node.js, Python, Superset/Looker