Bruno

Main role is to negotiate and define data products, features, and topology alongside business domains. Specify how to ingest, process and aggregate data. Define how the data product will be delivered to domain user, e.g., dashboards, table, Rest API. 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.

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

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

Responsible for defining the architecture and implementation of data products, i.e., recommendation platform, end-to-end. Development of data pipelines and deploy of ML models into production. Also mentoring 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.

Creation of data pipelines, deploy of ML models into production, collect business requirements from domain experts.

Stack: Azure, Spark, Python, AirFlow;

ML pipelines development and deployment, stack and data architecture definition;

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.

Over 8 years of experience working as an Industrial Engineer. Career change to Tech Industry in 2018.