Google Cloud Platform Data Architect

Data Strategy & Architecture Design

• Define Data Strategy: Collaborate with business stakeholders to understand data needs, identify key data sources, and define the overall data strategy aligned with business goals.
• Design Data Architectures: Create conceptual, logical, and physical data models for Google Cloud Platform services, including data warehousing, data lakes, and real-time streaming solutions.
• Select Google Cloud Platform Data Services: Evaluate and choose appropriate Google Cloud Platform services (e.g., BigQuery, Cloud Storage, Dataproc, Dataflow, Pub/Sub, Looker, Dataprep, Cloud SQL, Spanner) based on project requirements for performance, cost, scalability, and functionality.
• Develop Data Governance Framework: Design policies and procedures for data quality, data lineage, data cataloging, access control, and compliance.
• Design Data Integration Solutions: Architect data pipelines for ETL/ELT processes, streaming data ingestion, and API integrations.
• Design for Scalability & Performance: Ensure the architecture can handle current and future data volumes and user loads efficiently.
• Design for Security & Compliance: Implement robust security measures, including IAM, encryption, network security, and ensure compliance with relevant regulations (e.g., GDPR, HIPAA).
• Design for Cost Optimization: Recommend cost-effective solutions and monitor Google Cloud Platform spend related to data services.

Data Implementation & Development

• Oversee Data Pipeline Development: Guide and mentor teams in building ETL/ELT pipelines using tools like Dataflow, Dataproc, or custom scripts.
• Implement Data Warehousing & Lakes: Set up and configure data warehouses (BigQuery) and data lakes (Cloud Storage) with appropriate schemas and partitioning.
• Configure Streaming Data Solutions: Implement real-time data ingestion and processing using Pub/Sub and Dataflow.
• Implement Data Security Measures: Configure IAM roles, service accounts, encryption keys, and network controls for data services.
• Data Modeling & Optimization: Develop and optimize data models in BigQuery and other databases for query performance.
• Data Quality Implementation: Implement data validation and cleansing processes within pipelines.
• Tooling & Technology Evaluation: Assess and recommend new Google Cloud Platform data services or third-party tools that can enhance the data architecture.

Data Management & Operations

• Monitor Data Pipelines & Infrastructure: Oversee the performance, availability, and health of data services and pipelines.
• Troubleshoot Data Issues: Identify and resolve performance bottlenecks, data quality problems, and operational errors.
• Optimize Data Costs: Continuously analyze Google Cloud Platform data service costs and identify opportunities for optimization (e.g., BigQuery slot allocation, storage tiering).
• Manage Data Lifecycle: Implement strategies for data archiving, retention, and deletion.
• Ensure Data Security & Compliance: Regularly review and update security configurations and ensure ongoing adherence to compliance requirements.
• Capacity Planning: Forecast future data storage and processing needs and plan infrastructure accordingly.

Stakeholder Collaboration & Guidance

• Liaise with Business Users: Understand business needs and translate them into technical data solutions.
• Collaborate with Development Teams: Work closely with data engineers, software engineers, and data scientists.
• Provide Technical Guidance: Advise on best practices for data management, Google Cloud Platform services, and data modeling.
• Educate Teams: Train and mentor team members on Google Cloud Platform data services and architectural patterns.
• Present Solutions: Communicate architectural designs and strategies to technical and non-technical audiences.