Moaz E.

• Udarbejdede et fundament for en dataanalyseplatform ved hjælp af Airbyte, Google BigQuery, Apache Airflow, DBT og Looker;
• Konfigurerede Airbyte til strømlinet dataindlæsning.
• Anvendte Google BigQuery som det centrale datalager;
• Integrerede Apache Airflow til automatisering og styring af dataflows;
• Brugte DBT til at oprette og vedligeholde en strømlinet datamodellerings- og transformationsproces;
• Integrerede Looker til datavisualisering;
• Hjalp med fejlfinding og tekniske problemer.

Joined the data team at its inception, working on building the data platform from scratch. This included selecting the technology stack, setting the foundations, and applying best practices to deliver a platform that drives data-driven initiatives and supports business operations.

• Established Google BigQuery as the core data warehouse, optimizing data storage and retrieval.
• Designed and deployed an ELT pipelines using Airbyte on Kubernetes, facilitating seamless data extraction from sources like PostgreSQL and MongoDB, and leveraging CDC for efficient updates.
• Utilized Airflow for orchestrating jobs and dbt/Dataform for data transformations, ensuring smooth workflow automation.
• Automated CI/CD processes with GitHub Actions and Google Cloud Build, enhancing deployment efficiency.
• Implemented comprehensive data quality checks to ensure data integrity and governance.
• Leveraged Looker for business intelligence and reporting, delivering actionable insights to drive decision- making.

• Bidrog til udviklingen af en platform til maskinlæring og dataanalyse ved hjælp af Google Cloud Ecosystem, herunder BigQuery, Apache Airflow, Pub/Sub og Google Analytics;
• Brugte BigQuery til effektiv datalagring og -analyse;
• Integrerede Apache Airflow til automatisering og styring af dataflows;
• Implementerede Pub/Sub til pålidelige chatsamtaler og datastreaming i realtid;
• Indarbejdede Google Analytics til indsamling af indsigt fra brugerinteraktioner;
• Arbejdede med at optimere platformens ydeevne og skalerbarhed.

• Bidrog til udviklingen af en Big Data-platform inden for Cloudera-økosystemet, der omfatter Apache Spark, Hadoop, Apache Kafka, Apache Flume, Apache Hive og Impala;
• Designede robuste og skalerbare løsninger samt pipelines til dataindlæsning;
• Formulerede og automatiserede forbehandling og indtagelse af databatches og -strømme.

• Led the development of an adaptable video processing and analysis platform, enabling seamless integration of numerous Artificial Intelligence and Computer Vision algorithms for efficient real-time video analysis;

• Spearheaded the design and upkeep of the platform's pipeline, leveraging significant big data tools including Apache Storm, gRPC, RabbitMQ, Redis, and Kubernetes;

• Contributed to the creation of a comprehensive Data warehouse aggregating data from diverse sources;

• Conducted data analysis by executing intricate SQL Ad-Hoc Queries in response to business requirements from multiple origins;

• Developed SQL procedures to automate ETL tasks and workflows;

• Managed the complete data warehouse lifecycle, ensuring its continued functionality and relevance;