Alexandra Sampietro

Developed and maintained scalable batch and streaming ELT data pipelines using DBT and Google Cloud Platform (GCP) services like Bigquery SQL, Cloud functions, pubsub, dataflow (apache beam), to support key business domains including e- commerce, merchandising, and sales and provide C level dashboards. Led data architecture and modeling efforts for Revenue Growth initiatives, ensuring data quality, and scalability for a future-proof design of data products. Data exploration by using Dataiku and standard python libraries to provide accurate feature engineering insights for several models like client clustering, assortment allocations, delay forcasts, prototyped on BigQuery ML and setup in production in Vertex AI. Led the optimisation of ELT data pipelines for the Americas region, resulting in annual cost savings of €50K through reduced runtime and resource usage by leveraging dynamic programming airflow capabilities, Python and SQL code optimizations, and data quality test strategy set up.

As a data engineer directly attached to Moët Hennessy Data and AI direction I provide operational and technical expertise to design, build and govern the Möet Hennessy Data platform by: Establishing a documentation of data standards & best practices for the data platform. Establishing collaborative processes for cross-functional team engagement throughout project delivery lifecycles, ensuring seamless execution and alignment. Driving low-code tools implementation like Fivetran and Dataiku then establishing their best practices and governance strategy

Deployed and managed OLTP and OLAP pipelines for Airbus distributed micro-services application using messaging, video and geolocation data ingestion. Processing via Apache PySpark, while Apache Flink handles real-time video stream. MongoDB for file data management and parsing, and graph-based for geolocation relationships using Neo4j. Deploying pipelines and ressources with dataOps best practices using Gitlab CI and IaC with Terraform and Ansible. To orchestrate the microservices with Kubernetes. Building OLAP pipelines for near Real time recommandations system exposed through REST API in a in-house dashboard solution. Active participation in the development of a gun detection model using based on reaserch papers analysis with the help of CV librairies and pytorch to prototype solutions for security camera use cases.

As a software integration engineer I develop and integrate hardware, network and software systems to provide a full media streaming system for Defense and public safety Airbus Customers Integrating new streaming features and complex systems inside the existing Airbus systems Determining that the outcome of the integration has met the organization’s needs Educating the organization and clients about the system and software integration procedure, its implementation and its use. Handling a broad range of softwares and programming tasks to ensure that systems integrations are functional. Defining integration and testing strategies and tools and then planning them in accordance with established deadlines and time schedules. Testing automation development

Skills: TensorFlow