Natalia Pipas

• Led a team of three data engineers, fostering a collaborative environment focused on sharing knowledge and implementing best practices in data engineering.
• Planned and executed an AWS account migration using Terraform, prioritizing security and compliance requirements. This involved migrating infrastructure and data while ensuring adherence to industry standards and regulations.
• Designed and managed data pipelines using AWS Glue to support various analytical and machine learning use cases, including pricing optimization and churn prediction.

• Implemented a real-time bot detection solution using Apache Flink, resulting in cost savings and improved data quality for users. Managed multiple stakeholders to ensure organization-wide benefits.
• Lowered the entry barrier to the webshop's behavioral data by developing generic and user-friendly aggregations. Additionally, implemented a browser plugin for quick visualization of slot performance.
• Redesigned and implemented a cost-effective backup process, resulting in annual savings of over 30k. Converted the process into a reusable Airflow DAG, making it available to other teams.
• Developed Gitlab CI/CD templates, simplifying deployments across multiple projects and reducing development time

Led efforts to enhance recruitment analytics on a multidisciplinary team, driving data-driven insights and decision-making.

• Built batch and streaming data pipelines with Spark Streaming (Scala) and Airflow to aggregate and analyze behavioral data, enabling users to gain insights into their job marketing campaigns.
• Worked on choosing and building the company's data warehouse (Snowflake)
• Built APIs to provide analytics and product data to the job marketing platform. Technologies: Flask and Django
• Mentored back-end engineers on the team on data engineering's best practices