Location- New York City, NY
Job Description
Detailed JD:
Designs and builds scalable, secure, and cost-effective data solutions on the Google Cloud Platform. They bridge business requirements and technology by creating cloud-native data pipelines, data lakes, and data warehouses, while ensuring strict data governance and security.
Use this comprehensive, ready-to-use job description template to attract and evaluate top-tier data professionals for your cloud initiatives.
Role Overview
We are seeking an experienced GCP Data Architect to lead the design, modernization, and governance of our enterprise data platforms. You will collaborate with engineering, product, and business stakeholders to translate complex data needs into robust, scalable, and cloud-native solutions.
Core Responsibilities:
· Platform Architecture: Design and implement end-to-end data solutions, including modern data lakes, data warehouses, and batch/streaming data pipelines.
· Cloud Modernization: Lead the migration of on-premises or legacy data warehouses (e.g., Teradata, Oracle, Informatica) to GCP-native architectures.
· Data Governance & Security: Implement strict data security frameworks, data masking, and metadata management strategies to ensure compliance and privacy.
· Collaboration: Partner with data engineers, data scientists, and business analysts to optimize analytical workloads and support AI/ML initiatives.
· Best Practices: Establish CI/CD pipelines, automated deployments, and cost-optimization measures across the GCP ecosystem.
Required Qualifications & Tech Stack
· Experience: 8+ years of hands-on experience in data engineering or architecture, with at least 4–5 years focused on GCP-native environments.
· GCP Core Services: Deep knowledge of BigQuery (data warehousing), Cloud Data Fusion/Dataflow (ETL/ELT), Dataproc (Spark/Hadoop), and Pub/Sub (event streaming).
· Programming & Scripting: Strong proficiency in Python, SQL, and shell scripting.
· Orchestration & DevOps: Experience with workflow orchestration tools (e.g., Cloud Composer / Apache Airflow) and infrastructure-as-code (Terraform).
· Data Modeling: Solid understanding of dimensional modeling, star/snowflake schemas, and data mesh/data fabric concepts.
Preferred Qualifications
· Certifications: Google Cloud Certified Professional Data Engineer or Google Cloud Certified Professional Cloud Architect.
· Database Migration: Familiarity with migrating legacy databases (e.g., Oracle, SQL Server, Teradata) to the cloud.
· Data Governance: Practical experience using Dataplex for unified data governance
Thanks & Regards
Rahul Pandey
rahul.pandey@quantumworldit.com
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