Description
Deutsche Telekom processes global IoT SIM, lifecycle, usage, network, and partner connectivity data from multiple source systems and regions. The company modernizes legacy data pipelines in Microsoft Fabric and provides reliable, analytics-ready datasets for internal teams and partner-oriented use cases.
Responsibilities
•Build and maintain scalable data pipelines in Microsoft Fabric, including Lakehouse, Pipelines, Notebooks, Delta Lake, and Medallion Architecture, to process global IoT SIM data efficiently and reliably
•Develop ingestion and transformation workflows for Oracle, SAP, CSV, CDC, APIs, and CMP sources; structure data using Medallion Architecture (Bronze/Silver/Gold) and reusable data models
•Implement data quality checks, validation rules, monitoring, and automated error handling to ensure trusted datasets for reporting, analytics, and operational use cases
•Collaborate with architects, Product Owners, analysts, development, QA, and DevOps teams to industrialize data products, establish CI/CD practices, and support operational run models
•Design, implement, and maintain ingestion, transformation, and data quality workflows for SIM lifecycle, usage, network, and partner connectivity data from multiple source systems and regions
•Modernize and migrate data pipelines from legacy systems such as Oracle and SAP to Microsoft Fabric, integrate external Connectivity Management Platforms (CMPs), and provide reliable, analytics-ready datasets for internal teams and partner-oriented use cases
•Focus on hands-on data pipeline engineering, automation, monitoring, and operational reliability
Requirements
•Extensive hands-on experience with Microsoft Fabric or comparable Azure Data Engineering platforms, including Lakehouse patterns, Delta Lake, Data Pipelines, and notebook-based transformations
•Advanced Python, SQL, and PySpark skills for large-scale ingestion, transformation, migration, and optimization of IoT SIM, lifecycle, usage, and network telemetry data
•Experience with Medallion Architecture (Bronze/Silver/Gold), data modeling for analytical workloads, partitioning, performance tuning, and preparing reliable datasets for downstream use
•Hands-on experience with Data Quality frameworks, orchestration, monitoring, Git-based development, CI/CD, and production-ready Data Operations
Nice to have
•Hands-on experience with real-time or near-real-time streaming concepts such as Apache Kafka, Fabric Eventstream, and Event Hubs or similar technologies
•Background in Data Governance, Microsoft Purview, CMP integrations, telecommunications data models, secure Data Engineering patterns, or legacy SAP/Oracle modernization
•Experience with Power BI Semantic Models, Direct Lake, Self-Service Analytics Enablement, or controlled-access concepts such as Entra ID, OAuth2, Azure Key Vault, Private Endpoints, and Data Gateways
•DP-600, DP-203, or AZ-305 certifications
Conditions
•Part-time employment
