Analytics Engineer Semantic Layer

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 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 - / 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 analyticsSupersetMetabaseLookerPowerBI

Key Responsibilities:

1. Design build and own the end-to-end Semantic Layer - the single source of truth for all business metrics.
2. Architect and build Graph-based modelsKnowledge Graph to add context relationships and lineage to metrics.
3. Build secure high-performance APIsGraphQL/REST for internal and embedded analytics consumption.
4. Own pre-aggregation strategy caching and query performance tuning.
5. Implement enterprise-grade governance security and multi-tenant access control.
6. Partner with Data Engineering Product and Frontend teams to deliver self-serve data products.
7. Own documentation data quality and adoption of the semantic layer across the organization.

Tech Stack: dbt Snowflake/BigQuery Neo4j/GraphQL Airflow dbt Kubernetes TypeScript/ Python Superset/Looker