GCP Data Architect - S

Key Responsibilities

• Design and implement enterprise data architecture solutions on Google Cloud Platform (GCP).
• Develop scalable data lakes, data warehouses, and lakehouse architectures to support business intelligence, analytics, AI, and machine learning initiatives.
• Create data integration strategies for structured, semi-structured, and unstructured data sources.
• Define and enforce data modeling, governance, security, and data quality standards.
• Architect real-time and batch data processing pipelines.
• Lead cloud data migration and modernization initiatives from on-premises and legacy platforms.
• Collaborate with business stakeholders, data engineers, analysts, and data scientists to translate business requirements into scalable data solutions.
• Establish best practices for metadata management, master data management, lineage, and governance.
• Optimize data architecture for performance, reliability, scalability, and cost efficiency.
• Provide technical leadership, architecture reviews, and mentorship to engineering teams.

Key Technical Responsibilities

Google Cloud Data Services

• BigQuery
• Cloud Storage
• Dataflow
• Dataproc
• Pub/Sub
• BigLake
• Dataplex
• Cloud Composer
• Cloud SQL
• Spanner
• Bigtable
• Vertex AI (for AI-ready data architectures)

Data Engineering & Analytics

• Data Warehousing
• Data Lake and Lakehouse Architecture
• ETL / ELT Design
• Batch Processing
• Real-Time Data Processing
• Data Transformation Frameworks
• Advanced Analytics Platforms
• Data Catalog and Metadata Management

Architecture & Design

• Enterprise Data Architecture
• Cloud-Native Architectures
• Distributed Data Processing
• Event-Driven Architecture
• Data Mesh
• Data Fabric
• Microservices Integration
• API-Led Data Architecture