Angela B.

SUSE is a global open-source software leader delivering enterprise-grade Linux and cloud-native solutions to organizations worldwide.

• Designed and deployed automated data pipelines using Apache Airflow and AWS Glue to integrate enterprise data from SAP and Salesforce
• Built ETL/ELT workflows to transform and standardize source data, delivering production-ready datasets to Amazon Redshift for global analytics
• Enabled enterprise reporting by supporting Power BI dashboards used by global finance, sales, and operations teams
• Architected and deployed serverless data pipelines using Python-based AWS Lambda functions provisioned with Terraform
• Implemented scalable, fault-tolerant data processing pipelines to support growing data volumes while minimizing operational overhead

LendingTree is a leading online lending marketplace connecting consumers with financial products across loans, credit, and insurance.

• Led a full-scale migration from Microsoft SQL Server to PostgreSQL, redesigning data models, schemas, and indexing strategies to improve performance, scalability, and maintainability
• Architected and implemented the data platform using Azure Data Factory, Azure Synapse, and early-stage Microsoft Fabric
• Established enterprise data governance and metadata management using Microsoft Purview, Data Vault principles, and Unity Catalog, ensuring secure access control, data lineage, and schema consistency
• Deployed Apache Airflow on Astronomer for workflow orchestration and integrated CI/CD pipelines using GitLab Cloud
• Implemented automated schema, data quality, and infrastructure validation through Azure DevOps pipeline gates, enabling controlled promotion across development, test, and production environments
• Developed high-performance Python-based Airflow DAGs, optimized database performance, and led task delegation across the data engineering team
• Reduced data load times and pipeline code complexity by approximately 70%, accelerating data delivery and lowering operational overhead

7IM is an investment management firm providing data-driven solutions to support portfolio management, operations, and regulatory reporting.

• Designed and implemented a normalized enterprise data warehouse to support scalable, high-quality analytics across business domains
• Built end-to-end data pipelines using Azure Data Factory, Azure Storage, Databricks Autoloader, and Azure Data Lake
• Implemented streaming and incremental ingestion pipelines in Databricks to deliver reliable data for analytics and reporting
• Rebuilt and optimized ingestion pipelines during the migration to Azure Synapse Analytics, improving query performance and introducing automated data quality checks through Azure DevOps pipelines
• Implemented data governance, access control, and lineage using Unity Catalog
• Led the migration to Microsoft Fabric, modernizing the data platform and simplifying operational management

Hewlett Packard Enterprise is a global enterprise technology company delivering cloud, data, and infrastructure solutions to large-scale organizations across industries.

• Designed and implemented end-to-end ETL/ELT pipelines that consolidated data from more than 15 heterogeneous sources, including SAP, Salesforce, MongoDB, Hadoop, Oracle, Snowflake, APIs, and data lakes into a centralized PostgreSQL data warehouse
• Built automated ingestion and transformation workflows using AWS Glue, Azure Data Factory, Apache Airflow, dbt, and Dataiku, enabling reliable daily data refreshes for analytics, KPI reporting, and machine learning workloads
• Architected and deployed serverless data pipelines using AWS Lambda and Azure services, provisioned with Terraform, reducing manual operational effort by approximately 70%
• Led predictive machine learning initiatives in Dataiku, delivering models with up to 99% predictive accuracy and measurable business impact
• Served as technical lead for the data engineering team, coordinating task planning, code reviews, and collaboration with analytics, machine learning, and business stakeholders
• Established data quality, governance, and lineage standards to support enterprise reporting and regulatory compliance

NLB Bank is a leading financial institution operating across multiple European markets, providing retail and corporate banking services.

• Built secure and scalable data pipelines using Azure infrastructure, MSSQL, SSIS, and Python to process high-volume transactional and customer data.

• Integrated diverse data sources, including APIs, flat files, and cloud-based systems, ensuring consistency and reliability across financial datasets.

• Implemented regulatory-compliant data workflows aligned with GDPR and PCI-DSS requirements for sensitive financial and customer information.

• Enabled continent-wide real-time fraud alerting systems, significantly improving detection speed and operational response.

• Improved overall data processing performance and stability by optimizing failing jobs and refining ETL logic.

• Delivered enterprise reporting solutions using Power BI and SQL Server Reporting Services (SSRS), supporting global financial reporting needs.

Confidential digital marketing project focused on building scalable, cloud-native data platforms on AWS to support business-critical applications.

• Architected a fully automated ETL pipeline on AWS using S3, DynamoDB, Glue, DMS, and Step Functions to support a scalable data warehouse on Amazon Aurora.

• Led the migration of legacy on-premise data to AWS by automating schema conversion and data transfer with AWS DMS.

• Designed secure, high-performance data ingestion and transformation workflows to support application backends and analytics use cases.

• Improved system reliability and scalability by leveraging managed AWS services and event-driven orchestration patterns.

• Ensured data security, access control, and performance optimization across the cloud environment.

Deutsche Telekom is one of Europe’s largest telecommunications providers, serving millions of customers across mobile, broadband, and enterprise services.

• Supported senior data engineers in developing and maintaining enterprise data warehouse pipelines.

• Monitored existing production data workflows, identifying and debugging pipeline failures and performance issues.

• Assisted in maintaining Azure-based data platforms, ensuring data freshness and operational stability.

• Gained hands-on experience with enterprise-scale data engineering practices in a regulated telecom environment.