Data Engineer (m/f/d)

Jedox

1d•Hybrid

About The Position

At Jedox, we help businesses plan for a better future. Our Enterprise Performance Management (EPM) software empowers finance, sales, and other departments to budget, forecast, and analyze data—all in one intuitive platform. We’re on a mission to turn complex planning into #Superplännen: that amazing moment when everything goes exactly as you envisioned it.

Requirements

7+ years in data engineering, including 3+ years building enterprise-scale cloud platforms, with proven greenfield architecture and AI/ML data preparation experience.
Expertise in Python and SQL, and I have hands-on experience with Spark, Microsoft Fabric, the Azure Data Platform and Delta Lake.
Experienced in ETL/ELT, data modelling and warehousing.
Experience integrating enterprise systems (e.g., Salesforce, SharePoint, M365, Azure SQL/Data Lake) and working with REST APIs and modern data architectures.
Solid understanding of metadata management, master data management, and semantic modelling.
Excellent English communication skills are required.

Nice To Haves

Certifications in Azure/Fabric and experience with Purview, Synapse, Data Mesh, graph/vector databases, Azure AI Search, or event streaming.
Growth-oriented, proactive and driven by innovation, execution excellence and building impactful, scalable data solutions.

Responsibilities

Design and own the enterprise data platform: Build a scalable Microsoft Fabric data infrastructure using a medallion lakehouse architecture (Bronze, Silver or Gold) with Delta Lake.
Develop and operate data pipelines: Create robust batch and streaming ETL/ELT pipelines with schema evolution, automated transformation and strong data quality validation.
Integrate enterprise systems: Connect to data sources such as SharePoint, Salesforce, Microsoft 365, Azure SQL, ERP/CRM systems, file repositories and REST APIs.
Ensure performance and reliability: Optimize storage and processing across structured and unstructured data, including monitoring, alerting, and operational stability.
Build semantic data layers & governance: Establish a taxonomy and metadata standards, as well as semantic models and data cataloguing and lineage tracking. Enforce access control, compliance and security.
Drive AI readiness: Prepare data for AI use cases through document chunking, embedding pipelines, and vector-ready datasets for RAG.
Expose knowledge services & collaborate: Develop reusable APIs and data services for AI applications and work cross-functionally with AI, analytics, and business teams (#OneTeam).