Google Cloud Platform Data Architect

Arkhya Tech
New York, United States of America
7 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

New York, United States of America

Tech stack

Artificial Intelligence
Airflow
Computing Platforms
Google BigQuery
Cloud Database
Continuous Integration
Information Engineering
Data Governance
ETL
Data Masking
Data Security
Data Systems
Data Warehousing
DevOps
Dimensional Modeling
Data Flow Control
Hadoop
Python
Meta-Data Management
Oracle Applications
Cloudera
Shell Script
SQL Databases
Data Streaming
Teradata
Workflow Management Systems
Scripting (Bash/Python/Go/Ruby)
Google Cloud Platform
Informatica Powercenter
Spark
Data Lake
Deployment Automation
Cloud Migration
Terraform

Job description

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 Google Cloud Platform-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 Google Cloud Platform ecosystem.

Requirements

Experience: 8+ years of hands-on experience in data engineering or architecture, with at least 4 5 years focused on Google Cloud Platform-native environments.

Google Cloud Platform 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.

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