Cloud Data Engineer

What will be your challenge?

Join Bip, a company that works vertically on Google Cloud (GCP) platforms, specialized in advisory and innovative services in AI, Cloud, Data, and Martech. You will be responsible for operating on the GCP Data Platform, implementing batch and real-time processing pipelines, and transforming and processing structured and unstructured data.

You will need to be autonomous in data acquisition, historization, cleansing, anonymization/encrypting/masking of data, handling nulls and outliers, aggregation, structuring of unstructured data, and creating data quality KPIs, preparing data for users including: - Data Scientists who will use the datasets for ML model development - Data Visualization and Business Intelligence tools - Relational (e.g., DWH) and non-relational structures (e.g., Graph DB, NoSQL) - External applications in API logic (e.g., REST API)

You will also be responsible for receiving ML models from Data Scientists, optimizing them for scalable execution (e.g., applying parallelism), and integrating/scheduling them for automated, efficient execution. You will be a proficient professional who evolves to adapt to different contexts and implements complex pipelines, maximizing reuse, quality, efficiency, performance, documentation and order.

Cloud Data Engineer

What will be your challenge?

Join Bip, a company that works vertically on Google Cloud (GCP) platforms, specialized in advisory and innovative services in AI, Cloud, Data, and Martech. You will be responsible for operating on the GCP Data Platform, implementing batch and real-time processing pipelines, and transforming and processing structured and unstructured data.

You will need to be autonomous in data acquisition, historization, cleansing, anonymization/encrypting/masking of data, handling nulls and outliers, aggregation, structuring of unstructured data, and creating data quality KPIs, preparing data for users including: - Data Scientists who will use the datasets for ML model development - Data Visualization and Business Intelligence tools - Relational (e.g., DWH) and non-relational structures (e.g., Graph DB, NoSQL) - External applications in API logic (e.g., REST API)

You will also be responsible for receiving ML models from Data Scientists, optimizing them for scalable execution (e.g., applying parallelism), and integrating/scheduling them for automated, efficient execution. You will be a proficient professional who evolves to adapt to different contexts and implements complex pipelines, maximizing reuse, quality, efficiency, performance, documentation and order.