Data Engineer (m/f/d) Microsoft Fabric

Telekom; Województwo mazowieckie; On-site; PLN 180,000 - 250,000; Part time

Job summary

Telekom in Warsaw seeks a Data Engineer to build and maintain scalable data pipelines in Microsoft Fabric, including Lakehouse, Pipelines, Notebooks, Delta Lake, and Medallion Architecture for IoT SIM data.

You'll develop robust ingestion and transformation workflows from Oracle, SAP, CSV, APIs, and other sources, ensuring reliable datasets for analytics and operational use with CI/CD practices and strong data quality controls.

Qualifications

• Strong hands-on experience with Microsoft Fabric or comparable Azure data engineering platforms.
• Advanced Python, SQL, and PySpark for large-scale data ingestion and transformation.
• Experience with Medallion Architecture and data modeling for analytical workloads.

Responsibilities

• Build and maintain scalable data pipelines in Microsoft Fabric (Lakehouse, Pipelines, Notebooks).
• Ingest, transform, and validate IoT SIM data across Oracle, SAP, CSV, CDC, APIs, and CMP sources.
• Implement data quality checks, monitoring, and CI/CD practices for reliable datasets.

Skills

Python

SQL

PySpark

ETL pipelines

Microsoft Fabric

Data modeling

Data quality checks

Tools

Delta Lake

Git

CI/CD

Job description

Job ID: 3560644Data Engineer (m/f/d) Microsoft FabricWarsawProfessionalApplication deadline: 30.12.2026Regular (Part Time Possible)T-Hub PolskaYour TaskBuild 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 robust ingestion and transformation workflows for Oracle, SAP, CSV, CDC, APIs, and CMP sources, structuring data through 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.Your ProfileMust have:Strong 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 data 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 preparation of reliable datasets for downstream consumption.Practical experience with data quality frameworks, orchestration, monitoring, Git-based development, CI/CD, and production-grade data operations.Nice-to-haveHands-on experience with real-time or near-real-time streaming concepts, such as Apache Kafka, Fabric Eventstream, 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.Certifications: DP-600, DP-203, or AZ-305.About UsIn this role, you will 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. You will modernize and transition data pipelines from legacy systems such as Oracle and SAP into Microsoft Fabric, integrate external Connectivity Management Platforms (CMPs), and provide reliable, analytics-ready datasets for internal teams and partner-facing use cases. The role is focused on practical data pipeline engineering, automation, monitoring, and operational reliability.