3 beste Stellen als fullstack engineer (m/w/d) remote – aktuell auf Personalsuche

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

Integrity Next GmbH
Home Office

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Details zur Stelle

Festanstellung | Vollzeit
vor 9 Stunden

Qualifikationen

Power BI
Datenmodellierung
React
Tableau
Java
SQL
AWS
Machine Learning
PostgreSQL
REST
APIs
Apache
Kafka
GraphQL
SaaS
Künstliche Intelligenz
Python
Analytik

Vollständige Stellenbeschreibung

Mobile Work

Full-time

Permanent employee

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
Experience exposing data for BI, APIs, downstream product use cases, and AI or analytics consumption
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
Strong Python skills
Experience building APIs and services such as REST or GraphQL
Experience exposing data and tools through interfaces such as MCP servers
Solid AWS stack know-how
Experience with BI tools such as Amazon QuickSight, Apache Superset, Looker, Tableau, Power BI, or similar platforms
Strong understanding of how data should be structured for AI, analytics, semantic access, and product consumption
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
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

Communication & Collaboration

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

Nice to have

Some AI agent engineering experience, ideally with Amazon Bedrock AgentCore or frameworks such as LangChain, LangGraph, or Deep Agents
Some data science experience, such as statistics, feature engineering, experimentation, or machine learning
Experience with Snowflake Cortex AI
Experience supporting AI use cases through feature engineering, context shaping, or semantic enrichment
Experience with data quality, lineage, and validation practices
Experience in multi-team SaaS environments
Experience with unstructured data enrichment or hybrid structured and unstructured semantic models
General understanding of Java and/or frontend React

What do we offer?

Purpose & Impact

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

Attractive Benefits

30 days of paid vacation
EGYM Wellpass membership to support your work-life balance
Flexible working models to better balance work and personal life

Modern Work Environment & Flexibility

Inspiring office spaces in the heart of Munich
Flexible remote work from home or anywhere within Germany

Great Team

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

Flat Hierarchies & Ownership

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

About us

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.

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