Designing Data Processing Systems:

• Analyzing business requirements and translating them into technical data processing solutions.
• Designing for security and compliance (e.g., IAM, data encryption, data privacy with Cloud Data Loss Prevention API, data sovereignty).
• Designing for scalability and efficiency (e.g., assessing, troubleshooting, and improving data representations and data processing infrastructure).
• Designing data pipelines, including considerations for batch and streaming data processing, data publishing, and visualization.
• Planning data processing solutions, including infrastructure choice, availability, fault tolerance, and capacity planning.
• Migrating data warehousing and data processing from on-premises to cloud, including awareness of current state and validation of migration.
• Understanding data types, data structures, and data sources on GCP.

Building and Operationalizing Data Processing Systems:

• Selecting the appropriate storage technologies (e.g., Cloud Bigtable, Cloud Spanner, Cloud SQL, Cloud Storage, Firestore, Memorystore) and considering costs and performance.
• Building and operationalizing pipelines, including data cleansing, transformation, data acquisition and import, and integrating with new data sources.
• Building and operationalizing processing infrastructure, including provisioning resources, monitoring pipelines, adjusting pipelines, testing, and quality control.
• Deploying and managing data processing solutions using services like Cloud Dataflow (Apache Beam), Cloud Dataproc (Hadoop/Spark), Cloud Pub/Sub, and Cloud Composer (Apache Airflow).
• Implementing ETL (Extract, Transform, Load) processes to convert raw data into structured information.

Operationalizing Machine Learning Models:

• Leveraging pre-built ML models as a service and deploying ML pipelines.
• Choosing appropriate training and serving infrastructure (e.g., distributed vs. single machine, hardware accelerators).
• Measuring, monitoring, and troubleshooting machine learning models, including understanding ML terminology and common error sources.
• Integrating machine learning into data systems to predict trends and derive valuable insights, using services like Vertex AI and BigQuery ML.

Ensuring Solution Quality:

• Ensuring reliability and fidelity (e.g., performing data preparation and quality control with Cloud Dataprep, planning and executing data recovery, choosing consistency models).
• Ensuring flexibility and portability (e.g., hybrid cloud and edge computing, multi-cloud analytics with BigQuery Omni).
• Understanding development and operations best practices, including CI/CD pipelines, Cloud Build, and Cloud Workflows.
• Utilizing Cloud Logging, Cloud Monitoring, and other tools for observability and troubleshooting.