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.
Build the semantic foundation for data products
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Build and evolve the semantic layer in dbt and Snowflake semantic views, including business entities, metrics, dimensions, and reusable data models
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Define KPIs, business logic, canonical data definitions, and semantic consistency standards together with business and product stakeholders
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Help shape how semantic data products are exposed consistently across internal and external platform capabilities
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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
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Support AI use cases through feature shaping, context structuring, semantic enrichment, and business-grounded data preparation
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Collaborate with the AI Engineer to ensure agentic experiences are grounded in meaningful, well-structured business data
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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
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Ensure semantic models reflect fresh, reliable data from operational systems
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Align with solution teams on data contracts, source semantics, and integration expectations
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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
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Collaborate with platform, AI, solution, product, and business-facing teams
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Help the company build a reusable semantic layer that scales with future platform growth
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Apply spec-driven development, AI-assisted engineering workflows, and end-to-end production ownership
Experience & Domain Focus
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Very strong hands-on SQL skills and broad, deep database knowledge, including data modeling
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Strong hands-on experience with Snowflake, including Snowflake semantic views
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Hands-on experience with dbt at scale for transformations and analytics engineering best practices
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Experience with PostgreSQL as a source for structured business data
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Experience building semantic layers, reusable metrics, canonical data models, analytics engineering assets, KPIs, business logic, and data definitions with stakeholders
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Experience exposing data for BI, APIs, downstream product use cases, and AI or analytics consumption
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Experience defining or supporting data contracts, validation rules, semantic consistency standards, data quality, lineage, and trust practices
Technical / Methodological Skills-
Experience with near-real-time or CDC ingestion, ideally Snowflake Openflow or comparable tools such as Fivetran, Debezium, or Kafka
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Strong Python skills
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Experience building APIs and services such as REST or GraphQL
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Experience exposing data and tools through interfaces such as MCP servers
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Solid AWS stack know-how
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Experience with BI tools such as Amazon QuickSight, Apache Superset, Looker, Tableau, Power BI, or similar platforms
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Strong understanding of how data should be structured for AI, analytics, semantic access, and product consumption
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Comfortable with structured, spec-driven delivery and AI-assisted development workflows
Ways of Working & Mindset-
Fullstack ownership mindset with the ability to own capabilities from design and implementation to production operations
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Strong specialization in semantic and analytics engineering combined with the willingness to contribute across adjacent layers
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Ability to translate business meaning into reliable, reusable, and scalable data products
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Strong quality mindset around semantic consistency, validation, data trust, and long-term maintainability
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Comfortable working in an evolving platform environment that may expand toward unstructured data and lakehouse-style capabilities
Communication & Collaboration-
Ability to work closely with platform, AI, solution, product, and business-facing teams
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Strong collaboration skills for aligning on contracts, source semantics, KPI logic, and integration expectations
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Clear communication style when translating technical data models into business meaning
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Comfortable collaborating with Data & Platform Architecture, Data Engineering, AI Engineering, and solution teams
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Fluent English skills for working effectively in an English-speaking environment
Nice to have-
Some AI agent engineering experience, ideally with Amazon Bedrock AgentCore or frameworks such as LangChain, LangGraph, or Deep Agents
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Some data science experience, such as statistics, feature engineering, experimentation, or machine learning
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Experience with Snowflake Cortex AI
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Experience supporting AI use cases through feature engineering, context shaping, or semantic enrichment
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Experience with data quality, lineage, and validation practices
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Experience in multi-team SaaS environments
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Experience with unstructured data enrichment or hybrid structured and unstructured semantic models
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General understanding of Java and/or frontend React
A role with real meaning that is both enjoyable and impactful
The opportunity to make a sustainable contribution through your work
Attractive compensation as part of a growing company
A professional, welcoming, and highly motivated team
Collaboration at eye level with an open feedback culture
An environment where people support each other and grow together
Short decision-making paths and real opportunities to shape things
Freedom to contribute and implement your own ideas
A high level of ownership and responsibility
Please apply exclusively via our online application portal. This ensures a smooth and fair process for all candidates. Applications submitted by email or LinkedIn will not be considered. Thank you for your understanding.
IntegrityNext, a global leader in supply chain sustainability software, stands at the forefront of corporate sustainability and compliance. Since 2016, businesses have trusted IntegrityNext to simplify ESG compliance, reduce risks, and address critical challenges like due diligence, decarbonization, and sustainability reporting. With over 500 customers and 2 million suppliers across 190 countries, IntegrityNext is transforming supply chains into engines of transparency and sustainable growth. For more information, visit www.integritynext.com.
We are an equal opportunity employer and do not discriminate based on race, color, religion, gender, gender identity or expression, sexual orientation, national origin, genetics, disability, age, or veteran status. We welcome applicants from all backgrounds and strive to create an environment where everyone feels respected and heard. Join us in our mission to build a more equitable and inclusive world.
