Freelancers using Oracle SQL in Vienna
Freelancers using Oracle SQL in Vienna
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Mahdi Moaddel
Vienna, Austria
Data Engineer | Python ETL | SQL Automation | Airflow
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Data Engineer | Python ETL | SQL Automation | Airflow
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SQL Server Performance Optimization Project Industry: Financial Consulting, Technology Duration: 3 Months Project Cost: $6,000 Started On: September 2024 Optimized enterprise SQL Server databases suffering from severe performance bottlenecks affecting business reporting and transactional operations. The project focused on analyzing execution plans, optimizing indexing strategies, refactoring heavy SQL queries, and improving database architecture for scalability and reliability. I conducted deep SQL tuning analysis and redesigned inefficient queries that were causing excessive CPU utilization and long-running report generation. Results: • Reduced complex query execution time by over 80% • Improved database scalability • Enhanced reporting performance and reliability • Reduced server resource consumption Technologies: SQL Server, T-SQL, Oracle PL/SQL, PostgreSQL, SSMS
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Power BI Executive KPI Dashboard Suite Industry: Business Services, Data Analytics Duration: 4 Months Project Cost: $7,500 Started On: March 2025 Created advanced Power BI dashboards and executive reporting solutions for real-time KPI tracking and strategic business analysis. The client required interactive dashboards for monitoring sales, operations, financial performance, and operational efficiency across multiple departments. I integrated multiple data sources, automated reporting pipelines, and designed visually optimized dashboards for executive management. Features included: • Interactive KPI dashboards • Automated data refresh • Advanced filtering and drill-down analytics • Executive-level reporting visuals • Performance monitoring metrics Technologies: Power BI, SQL Server, Azure SQL, PostgreSQL, Python
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Real-Time Kafka Streaming Analytics System Industry: Technology, Software, Data Analytics Duration: 5 Months Project Cost: $9,500 Started On: June 2024 Developed a real-time event streaming and analytics platform for processing high-volume transactional data with minimal latency. The client needed a scalable architecture capable of handling live business events, processing streaming data in real time, and generating operational insights instantly. I designed a distributed Kafka-based event pipeline and developed Python consumers for processing and transforming incoming data streams. The system included fault-tolerant stream processing, automated monitoring, Docker-based deployment pipelines, and scalable microservices architecture orchestrated through Kubernetes. Key achievements: • Built a scalable event-driven architecture • Enabled real-time analytics and monitoring • Improved system reliability and fault tolerance • Reduced latency in data processing operations Technologies: Apache Kafka, Python, Docker, Kubernetes, PostgreSQL, GitLab CI/CD
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Enterprise ETL & Data Warehouse Platform Designed and developed a scalable enterprise ETL and data warehousing platform for consolidating large volumes of structured and semi-structured business data into a centralized analytics ecosystem. The client required a high-performance reporting infrastructure capable of integrating data from multiple operational systems while improving reporting speed, data quality, and business visibility. I designed automated ETL workflows using Python and Apache Airflow, implemented optimized relational database models, and developed Power BI dashboards for executive-level KPI monitoring. Major responsibilities included database schema design, SQL performance optimization, API integrations, incremental data loading strategies, data transformation workflows, and automated reporting pipelines. One of the main challenges involved handling inconsistent legacy data structures and improving performance for high-volume analytics queries. I solved this by redesigning indexing strategies, implementing partitioning techniques, and optimizing query execution plans. Results: • Reduced reporting time by more than 70% • Improved data consistency across departments • Automated manual reporting workflows • Increased scalability and maintainability of the data platform
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