GCP Data Platform Architect

Unclassified - Beaverton, OR

Part-time - Senior

Beaverton, OR

About the position

The GCP Data Platform Architect at Accenture is responsible for leading technology innovation by designing, implementing, and managing scalable and secure data solutions on the Google Cloud Platform (GCP). This role involves collaborating with various teams to translate data requirements into robust architectural blueprints, ensuring optimal performance and security of data platforms. The position offers opportunities to work on complex business challenges and contribute to exciting client initiatives in a collaborative and inclusive environment.

Responsibilities

• Design and implement end-to-end data solutions on GCP, including data ingestion, storage, processing, transformation, and analytics.
• Architect, design, and deploy data warehouses and data lakes using technologies like BigQuery, Dataflow, and Dataproc.
• Design and implement big data solutions on GCP, including data pipelines, streaming analytics, and machine learning workflows.
• Establish and maintain robust data security frameworks, implement access controls, and ensure data governance practices.
• Monitor, analyze, and optimize data platform performance for efficiency and cost-effectiveness.
• Stay updated on the latest GCP data technologies, evaluating and recommending their adoption within the organization.
• Collaborate with data engineers, data scientists, business analysts, and other stakeholders to understand requirements and deliver optimal solutions.
• Develop clear and comprehensive documentation, including architectural diagrams, design specifications, and operational guidelines.

Requirements

• Minimum of 3+ years of professional experience with GCP data services, including BigQuery, Dataflow, Dataproc, Cloud Storage, Pub/Sub, and related technologies.
• Minimum of 6+ years of strong proficiency in data warehousing concepts, data modeling, and ETL/ELT processes.
• Minimum of 6+ years of professional experience with big data technologies.

Nice-to-haves

• Understanding of LLMs, Prompt engineering, Model Evaluation & Fine-tuning, ML ops, and Ethical & responsible AI.