GCP Architect

Responsibilities

• Lead discussions with business and functional analysts to understand requirements and assess integration impacts on business architecture.
• Prepare technical architecture and design, clarify requirements, and resolve ambiguities.
• Develop solutions in line with established technical designs, integration standards, and quality processes.
• Create and enforce technical design and development guides, templates, and standards.
• Design templates or scripts to automate routine development or operational tasks.
• Design end-to-end data architectures on GCP, ensuring scalability, performance, and cost efficiency.
• Define best practices for data governance, metadata management, and compliance.
• Implement frameworks for data quality, lineage, cataloging, and security.
• Collaborate with stakeholders to enforce governance policies across data pipelines.
• Architect and optimize ETL/ELT pipelines using GCP services (e.g., Dataflow, Dataproc, Big Query, Pub/Sub, Cloud Composer).
• Ensure efficient ingestion, transformation, and storage of large‑scale datasets & Databricks Integration.
• Design and manage Databricks clusters for advanced analytics, machine learning, and big data processing.
• Optimize Spark‑based workflows for performance and cost.

• Proven experience as a Cloud Architect (preferably GCP‑certified).
• Strong knowledge of data management principles and data governance frameworks.
• Hands‑on expertise with Big Data technologies (Spark, Hadoop, Kafka, Databricks).
• Proficiency in ETL pipeline design and optimization.
• Deep understanding of GCP services:
  Big Query, Dataflow, Dataproc, Pub/Sub, Cloud Storage, IAM, Cloud Composer.
• Experience with Databricks for big data analytics and ML workflows.
• Strong problem‑solving, communication, and stakeholder management skills.

• Google Cloud Professional Cloud Architect certification.
• Experience with multi‑cloud or hybrid architectures.
• Familiarity with data security and compliance standards (GDPR, HIPAA, etc.).
• Knowledge of Dev Ops practices and CI/CD pipelines for data solutions.