Data Engineering Manager

Ready to shape Frey’s data engineering capability and play a key role in a multi-year product and technology transformation?

As Data Engineering

Manager, you will build and lead the team behind the data platforms powering our products, analytics, and AI-driven optimization. Data is a core strategic asset at Frey, driving margin, scalability, and competitive advantage. Make a smarter move and unleash your potential as a Data Engineering Manager at Frey.

Role & Responsibilities:

We are looking for a "context-first" leader who understands that modern data engineering is not just about pipelines and moving bytes, but about architecting meaning. To succeed you balance deep engineering roots in Azure cloud ecosystems with the creative technical agility required to build graph-based semantic layers. You possess the architectural maturity to manage mission-critical ERP data while staying hands-on with the protocols, like MCP, that are redefining how AI interacts with enterprise knowledge.

We value leaders who view data as a strategic asset that must be modeled for both human insight and machine reasoning, ensuring that every pipeline contributes to a trustworthy "Digital Twin" of our operations. In this role, your ability to translate complex analytics needs into a hybrid platform that supports both traditional BI and agentic AI is essential.

Main responsibilities

Multidisciplinary Leadership: Build and lead a team of engineers, scientists, and analysts, balancing high-speed delivery with technical coaching and performance development.

Hybrid Architecture Ownership: Design end-to-end data products that support both structured relational reporting and unstructured semantic layers (Knowledge Graphs/Vector stores).

AI-Ready Integration: Build scalable pipelines from core systems and external providers optimized for real-time AI agent consumption.

Semantic Modeling & Protocols: Evolve transformation layers into robust semantic models using protocols like MCP to enable advanced GraphRAG and AI reasoning.

Standards & Observability: Establish rigorous frameworks for data quality, testing, and lineage to ensure “truthfulness” and auditability in both BI and AI outputs.

Expected skills & Experience:

In this role, your ability to translate data and analytics needs into a scalable, reliable data platform with clear business impact is essential. You will be expected to balance hands-on technical leadership with a product- and outcome-oriented mindset, enabling teams and stakeholders to actively make smarter, automated decision-making across the business.

We are looking for

The Hybrid Architect: Proven experience leading data teams in Azure environments, with a deep interest in the intersection of Data Engineering and GenAI.

Dual-Competency: Strong hands-on background in traditional SQL/Lakehouse modeling combined with a working knowledge of Ontologies, Knowledge Graphs, and Vector search.

Protocol Literacy: Familiarity with modern AI connectivity standards and an understanding of how to structure data to minimize hallucination.

Product-Minded Leader: Ability to partner with Software Engineering and Product Managers to translate business goals into scalable, reliable data products.

Pragmatic Innovator: An outcome-driven mindset that prioritizes data integrity and business impact in a fast-moving technical landscape.

Experience with data platforms supporting supply chain, logistics, trading, or financial systems can be highly valuable. Exposure to streaming data, event-driven architectures, or real-time analytics, as well as enabling machine learning, optimization, or advanced analytics use cases in production environments, is also beneficial.