As a Google Cloud Platform (GCP) Data Engineer, your roles and responsibilities typically involve designing, building, and maintaining scalable data processing systems and pipelines to support data analytics and machine learning initiatives. Here's a breakdown of the key roles and responsibilities:

Data Infrastructure Design: Designing scalable and reliable data infrastructure on GCP, including data lakes, data warehouses, and streaming data pipelines.

Data Pipeline Development: Developing ETL (Extract, Transform, Load) processes and data pipelines to ingest, process, and transform data from various sources into usable formats for analytics and reporting.

Big Data Technologies: Working with various big data technologies on GCP such as BigQuery, Dataflow, Dataproc, and Pub/Sub to process and analyze large volumes of data efficiently.

Data Modeling and Schema Design: Designing data models and schemas to support analytical and reporting requirements, ensuring data quality, integrity, and consistency.

Data Integration: Integrating data from different sources, including databases, APIs, and third-party services, into the data ecosystem on GCP.

Performance Optimization: Optimizing data pipelines and queries for performance, scalability, and cost-effectiveness, considering factors like data volume, velocity, and variety.

Data Governance and Security: Implementing data governance policies, access controls, and encryption mechanisms to ensure data security and compliance with regulatory requirements.

Monitoring and Troubleshooting: Monitoring data pipelines and infrastructure components for performance, availability, and reliability, and troubleshooting issues as they arise.

Collaboration: Collaborating with cross-functional teams including data scientists, analysts, and business stakeholders to understand data requirements and deliver solutions that meet business objectives.