Nour is a talented Data Engineer with over 5 years of experience in the IT industry. He excels in developing search and visualization dashboards, using tools like Kafka, Solr, and Elasticsearch, and taking decisive action based on the insights gained. Nour is also skilled in creating efficient data flow pipelines, including data enrichment, database persistence, and API creation. With a passion for driving data-based decision-making, Nour is a valuable addition to any team.
Experience:
Skilled at developing scalable data pipelines using Apache Spark, Airflow, and AWS Glue. He improved processing time by 30% by designing a complex Spark data pipeline that handled over 10TB of data per day.
Experienced in data warehouse and ETL processes, ensuring data quality and integrity. He maintained an Amazon Redshift data warehouse and implemented rigorous validation and transformation rules to ensure data quality and integrity.
Expertise in working with complex datasets, SQL and NoSQL databases, and big data technologies. Mainly, he analyzed over 2TB of social media data for a consumer brand using Hadoop and Hive. This resulted in actionable insights that drove a 15% increase in customer engagement.
Experienced in designing and implementing cloud-based data solutions using AWS, Azure, and Google Cloud Platform. Using AWS S3, Glue, and Athena, he built a highly available, scalable data pipeline that reduced infrastructure costs and enhanced operations.
Specialization
.NET development
Data Warehousing
Microsoft SQL Server
(CRM) system development and implementation
Expertise
Languages: Python; C/C++; C#; JavaScript; SQL, Ruby on Rails.
Frameworks and Tools: openAI Gym; deap; Mysql; SQL server; Sqlalchemy; Alembic; Pandas ; PowerBI; Banana; Kibana; Elasticsearch; Solr; Scikit learn; Django; Flask; Json; git ; docker; PyCharm; requests; Matplotlib; Folium; Seaborn; Vega and Vega lite Json language; Kafka; Snowplow; Retool, GCP, Azure (Databricks, Datafactory, blob storage, DataLakeGen2), AWS (S3), Shooju.