Bruno

Main role is to negotiate and define data products, features, and topology alongside business domains. Specifying how to ingest, process and aggregate data.  Coding frameworks, pipelines or database creations, while helping define standards for the data platform.

• Supporting and mentoring junior engineers.
• Data architecture design, data products deployment, translating business requirements into technical specifications.
• Defining how the data product will be delivered to domain user, e.g., dashboards, table, Rest API.

Stack: AWS, Kubernetes, Spark, Scala, Python, ksqlDB, MongoDB, REST API

Fraud data engineer responsible for defining the fraud data flow (ingestion, processing, storage) based on business requirements. Responsible for moving machine learning models into production (creating pipelines or Rest APIs to deploy the models) and mentoring junior engineers.

• Data architecture design, ML models deployment, automation, and creation of ML pipelines

Stack: AWS, Kubernetes, Kubeflow, Spark, Scala, Python, Clojure, REST API, Machine Learning

Defined the architecture and implementation of data products, i.e., recommendation platform, end-to-end. Developed data pipelines and deployment of ML models into production.

• Mentored junior engineers.
• Data architecture design, ML pipelines development, and deployment, data products design and deployment;

Stack: AWS, Spark, Python, Terraform, Git, Airflow, Docker, Kubernetes, MLFlow, Machine Learning

Created data pipelines, and deployed ML models into production end-to-end.

• Collected business requirements from domain experts.
• ML pipelines development and deployment, stack and data architecture definition.

Stack: Azure, Spark, Python, AirFlow, Machine Learning

Business Intelligence Analyst, responsible for collecting businesses requirements and converting into managerial dashboards (Power BI)

• Customer-facing role, development and implementation of business analytics and policies.