Job
- Level
- Experienced
- Job Field
- Web, Data, Full Stack
- Employment Type
- Full Time
- Contract Type
- Permanent employment
- Location
- Munich
- Working Model
- Hybrid, Onsite
Job Summary
In this role, you will develop semantic data models and define KPIs and business logic to make data usable for BI tools and APIs, while closely collaborating with AI and solution teams.
Job Technologies
Your role in the team
- 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 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.
- 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 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.
- Helfen Sie bei der Definition von Validierungsregeln, Datenvertrauenspraktiken, Liniennachverfolgung und Konsistenzkontrollen.
- 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.
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Our expectations of you
Qualifications
- Sehr ausgeprägte praktische SQL-Kenntnisse und umfassendes, tiefgehendes Datenbankwissen, einschließlich Datenmodellierung.
- Strong Python skills.
- Solid AWS stack know-how.
- Starkes Verständnis dafür, wie Daten für AI, Analytics, semantischen Zugriff und Produktkonsum strukturiert sein sollten.
- Comfortable with structured, spec-driven delivery and AI-assisted development workflows.
- Fullstack ownership mindset with the ability to own capabilities from design and implementation to production operations.
- Strong specialization in semantic and analytics engineering combined with the willingness to contribute across adjacent layers.
- Ability to translate business meaning into reliable, reusable, and scalable data products.
- Strong quality mindset around semantic consistency, validation, data trust, and long-term maintainability.
- Comfortable working in an evolving platform environment that may expand toward unstructured data and lakehouse-style capabilities.
- Ability to work closely with platform, AI, solution, product, and business-facing teams.
- Strong collaboration skills for aligning on contracts, source semantics, KPI logic, and integration expectations.
- Clear communication style when translating technical data models into business meaning.
- Comfortable collaborating with Data & Platform Architecture, Data Engineering, AI Engineering, and solution teams.
- Fluent English skills for working effectively in an English-speaking environment.
Experience
- 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.
- Experience exposing data for BI, APIs, downstream product use cases, and AI or analytics consumption.
- Experience in defining or supporting data contracts, validation rules, semantic consistency standards, data quality, lineage, and trust practices.
- Experience with near-real-time or CDC ingestion, ideally Snowflake Openflow or comparable tools such as Fivetran, Debezium, or Kafka.
- Experience building APIs and services such as REST or GraphQL.
- Experience exposing data and tools through interfaces such as MCP servers.
- Experience with BI tools such as Amazon QuickSight, Apache Superset, Looker, Tableau, Power BI, or similar platforms.
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What we offer
- 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.
- 30 days of paid vacation.
- EGYM Wellpass membership to support your work-life balance.
- Flexible working models to better balance work and personal life.
- Inspiring office spaces in the heart of Munich.
- Flexible remote work from home or anywhere within Germany.
- 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.
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Benefits
Work-Life-Integration
Topics that you deal with on the job
Job Locations
This is your employer
Integrity Next GmbH
Integrity Next GmbH, based in Munich, offers an innovative SaaS platform for ensuring compliance and sustainability in international supply chains. It assists companies in identifying ESG risks and monitoring their suppliers.
Description
- Company Type
- Established Company
- Working Model
- Hybrid, Onsite
- Industry
- Internet, IT, Telecommunication
Fullstack Engineer Semantic Analytics
- Location
- Munich
- Working Model
- Hybrid, Onsite
- Diversity
- Open for all genders
- English Only
- English only required
