Fullstack Engineer (Semantic / Analytics) (m/f/d)

Join us!

At IntegrityNext, we are building a shared AI and data platform on AWS on top of our supply chain and product compliance platform. The platform powers semantic data access, BI, APIs, and agentic product experiences.

Our PostgreSQL operational databases are ingested into Snowflake through a near-real-time pipeline built on Snowflake Openflow. On top of Snowflake, we transform and model data with dbt, expose business meaning through Snowflake semantic views, use Snowflake Cortex AI for AI capabilities, and surface curated data through Amazon QuickSight, Apache Superset, APIs, and AI consumption layers.

As Fullstack Engineer (Semantic / Analytics) (m/f/d), you will own the business meaning of data and make it reusable across analytics, BI, APIs, semantic access, and AI-powered experiences. You will work across semantic modeling, KPI logic, reusable data models, business-facing data exposure, and collaborate closely with platform, AI, solution, and business teams.

The platform will continue to evolve toward broader support for unstructured data and lakehouse-style capabilities. We work spec-driven, use AI-assisted engineering tools such as Claude Code and Cursor, follow “You build it, you run it”, and expect strong specialization combined with fullstack ownership.

What can you expect?

Build the semantic foundation for data products

• Build and evolve the semantic layer in dbt and Snowflake semantic views, including business entities, metrics, dimensions, and reusable data models
• Define KPIs, business logic, canonical data definitions, and semantic consistency standards together with business and product stakeholders
• Help shape how semantic data products are exposed consistently across internal and external platform capabilities
• Ensure business entities, KPIs, and metrics are clearly and consistently defined across the platform

Make curated data usable across BI, APIs, and AI* Expose curated data for BI tools such as Amazon QuickSight and Apache Superset, APIs, downstream product use cases, and AI consumption including Snowflake Cortex AI

• Support AI use cases through feature shaping, context structuring, semantic enrichment, and business-grounded data preparation
• Collaborate with the AI Engineer to ensure agentic experiences are grounded in meaningful, well-structured business data
• Help ensure BI, APIs, and AI use cases rely on the same trusted semantic foundations in Snowflake

Work with reliable, fresh, and governed data* Work with near-real-time data ingested from PostgreSQL into Snowflake via Snowflake Openflow

• Ensure semantic models reflect fresh, reliable data from operational systems
• Align with solution teams on data contracts, source semantics, and integration expectations
• Help define validation rules, data trust practices, lineage support, and consistency controls

Collaborate across platform, product, and engineering* Work closely with the Data & Platform Architect and Data & Platform Engineer to build semantic models on reliable, scalable Snowflake foundations

• Collaborate with platform, AI, solution, product, and business-facing teams
• Help the company build a reusable semantic layer that scales with future platform growth
• Apply spec-driven development, AI-assisted engineering workflows, and end-to-end production ownership

What should you bring along?

Experience & Domain Focus

• Very strong hands-on SQL skills and broad, deep database knowledge, including data modeling
• Strong hands-on experience with Snowflake, including Snowflake semantic views
• Hands-on experience with dbt at scale for transformations and analytics engineering best practices
• Experience with PostgreSQL as a source for structured business data
• Experience building semantic layers, reusable metrics, canonical data models, analytics engineering assets, KPIs, business logic, and data definitions with stakeholders
• Experie