19.08.2026 aktualisiert
Premiumkunde
40 % teilweise verfügbarSenior Cloud Data Engineer & Architect | Microsoft Fabric | Databricks | Data Platforms & DWH
Frankfurt am Main, Deutschland
Weltweit
Bachelor of Engineering in Computer scienceÜber mich
Senior Cloud Data Architect & Data Engineer with 15+ years of experience. Expert in Microsoft Fabric, Databricks, Snowflake, PySpark, Python, dbt & Airflow. Proven track record designing scalable cloud DWH, Lakehouse and enterprise data platforms for leading German enterprises.
Skills
Apache AirflowData ArchitectureData IntegrationData WarehousingAzure Data FactorySnowflakeApache SparkMicrosoft Fabric (Analytics-Plattform)Data LakePySparkAzure Synapse AnalyticsDatabricks
Senior Cloud Data Engineer & Architect with 15+ years of experience designing, modernizing and implementing enterprise Data Warehouse, Lakehouse and cloud data platforms. I specialize in Microsoft Azure, Databricks, Microsoft Fabric and Snowflake, combining strong architecture expertise with hands-on engineering and delivery.
I have extensive experience delivering business-critical data solutions for leading organizations in banking, financial services, energy, retail and travel, including projects for Deutsche Bank, Deutsche Börse, Commerzbank, Uniper, ALDI SÜD and HRS.
Core Expertise:
Cloud Data Architecture
- Enterprise Data Warehouse & Lakehouse architecture
- Azure and cloud data platform modernization
- Microsoft Fabric architecture and migration
- Databricks Lakehouse solutions
- Snowflake Data Warehouse & ELT architecture
- Data modeling and scalable data-platform design
Data Engineering & Integration
- Azure Data Factory (ADF) / Fabric Data Factory
- Azure Databricks, PySpark & Python
- Microsoft Fabric
- Snowflake & SnowSQL
- dbt
- Apache Airflow
- Azure Synapse Analytics
- SQL Server
- SAP HANA / SAP data integration
- REST API and enterprise application integration
Modernization & Migration
- Legacy DWH/ETL to modern cloud platforms
- ADF and Azure workload modernization
- Migration of orchestration workloads to Apache Airflow
- Design of reusable ingestion and transformation frameworks
- Batch and API-based data integration
- Performance and cost optimization
I work across the complete data-platform lifecycle: requirements analysis, architecture, technology selection, data modeling, ingestion, transformation, orchestration, implementation, testing, CI/CD, performance optimization and production support.
My strength is bridging architecture and hands-on engineering. I can define the target architecture and technical standards while also working directly with SQL, Python, PySpark, Databricks, Snowflake, Fabric, dbt and Airflow to implement the solution.
Having worked for many years on complex projects in Germany, I am experienced in enterprise environments with demanding requirements around data quality, reliability, scalability, security and maintainability.
I am particularly interested in projects involving:
Azure Data Platforms | Databricks | Microsoft Fabric | Snowflake | Cloud DWH | Lakehouse | Data Platform Modernization | Data Architecture | Data Engineering | dbt | Airflow | PySpark | SAP-to-Cloud Integration
Sprachen
DeutschGrundkenntnisseEnglischMuttersprache
Projekthistorie
Responsibilities:
Data Pipeline/ELT:
- Design, Develop and Maintain ETL/Data pipelines using Azure Data Factory and Python.
- Designed and led the implementation of end-to-end data pipelines on Azure Data Factory, ensuring efficient data movement and transformation across multiple sources. Resulted in a 30% reduction in data processing time and improved data accuracy.
- Setup all meta data tables, their configurations, store procedures, views for pipeline reusability to load using Generic Import pipelines.
- Reduced 60% to 70% development time of source to data lake and data lake to staging lay mappings by developing generic ADF pipelines.
- Setup all database objects needed for logging pipeline run information.
- Creation of ADF Linked Services, Data Sets, pipelines to read data from SAP tables using SAP Table linked service and load data into Azure Data Lake Storage Gen2.
- Creation of various types of Data Sources, Linked Services, Pipelines, Global Variables, Triggers, etc ADF objects required for pipeline development.
- Creation of Global, Linked Service, Data Source, pipeline parameters for reusability.
- Create ADF pipelines using various activities like Copy Data, Web, Lookup, foreach, store procedure, execute pipeline, etc.
- Uses various data flow transformations such as select, filter, join, derive column, exists, sequence, etc.
- Create ADF Self Hosted Runtime and read data from on premises source system like SAP, etc.
Contract Type: Contract
Role: Data Engineer
Project: Energy Data Lake
Project Technology Stack
Cloud Applications: Microsoft Azure
Source System: REST API, MS SQL Server, Snowflake, CSVs, XMLs
Target System: MS SQL Server, Snowflake
ETL Tool/Programming Language: Azure Data Factory, Talend Data Integration, Python
Other programming languages: Python, SQL, SnowSQL
Scheduling Tool: Azure Batch Service, Talend Management Console
Role: Data Engineer
Project: Energy Data Lake
Project Technology Stack
Cloud Applications: Microsoft Azure
Source System: REST API, MS SQL Server, Snowflake, CSVs, XMLs
Target System: MS SQL Server, Snowflake
ETL Tool/Programming Language: Azure Data Factory, Talend Data Integration, Python
Other programming languages: Python, SQL, SnowSQL
Scheduling Tool: Azure Batch Service, Talend Management Console
- Implement data pipelines using Azure Data Factory
- Migration from the old system to the new system
- Connect, process, implement and store data sources
- Data processing with Azure MS SQL database
- Create SQL procedures that contain the data processing logic
- Migration from the old system to the new system
- Connect, process, implement and store data sources
- Data processing with Azure MS SQL database
- Create SQL procedures that contain the data processing logic