Angela Bogdanovska

• Lead Architect, Solution Designer, Senior Data Engineer, and Team Lead for end-to-end development of scalable database architecture.
• Directed deployment of Airflow and Astronomer for advanced data orchestration.
• Implemented CI/CD pipelines in GitLab Cloud to streamline development and deployment processes.
• Authored and optimized Python scripts for DAGs, ensuring seamless data flow.
• Delegated tasks to team members and oversaw database performance with a focus on monitoring and continuous improvement.

IT consulting firm & technology service provider

• Developed end-to-end data pipelines (ETL and ELT) and established data warehousing solutions from scratch.
• Integrated diverse data sources, including relational and non-relational databases (SFTP, MongoDB, Hadoop, Snowflake, Oracle, AWS, PostgreSQL), APIs, JSON, and CSV/TSV files.
• Utilized third-party providers like Adobe, Salesforce, and SAP for data extraction and integration.
• Performed data transformations using Python and SQL for effective processing of raw data.
• Implemented automated data pipelines, executed daily, leveraging tools such as Azure, Airflow, Dataiku, and Talend to facilitate data integration and loading into the PostgreSQL data warehouse.

• Orchestrated the complete setup of a data warehouse in AWS RDS Aurora PostgreSQL, toke sole responsibility of the crucial decisions related to selecting appropriate CPU configurations, instances, memory allocation, and storage units
• Established the foundation of the data warehouse and successfully make the migration data from Snowflake as well as like a primary data source
• Created accounts and connections for the rest of the engineers and subsequently handed over the data warehouse for ongoing maintenance.

• Designed and implemented a robust ETL pipeline using Azure services, including Azure Blob Storage, Cosmos DB, Data Factory, and SQL Database or Synapse Analytics.
• Automated data extraction, transformation, and loading processes with Azure Data Factory, ensuring efficient integration and high performance.
• Managed data warehousing and analytics, delivering a scalable and secure solution optimized for advanced reporting and business intelligence.

• Architected a comprehensive ETL pipeline using AWS services, including S3, DynamoDB, Glue, and DMS, to build a scalable Data Warehouse hosted on AWS RDS (Aurora).
• Automated data migration using Schema Converter and AWS DMS from an existing database.
• Streamlined data extraction, transformation, and loading with AWS Step Functions and AWS Glue, ensuring seamless integration, high performance, and secure data management.
• Optimized data querying and analysis using AWS Aurora, supporting advanced analytics and reporting.

• Supporting other data engineers in building data-warehouse pipelines, mostly taking care or roles and watching the already existing data pipelines.