GCP Data Engineer – Remote

Overview

We are seeking a highly skilled and motivated Senior GCP Data Architect to join our dynamic team remotely. In this pivotal role, you will lead the design, development, and implementation of scalable data solutions leveraging Google Cloud Platform (GCP) and other cloud services.

Google Cloud Platform (GCP)
PySpark
Python
Airflow
Hive
Kafka
Data Pipeline Development, Enhancement & Maintenance

Design, build, and maintain scalable ETL/ELT pipelines.
Automate data ingestion from multiple data sources.
Ensure reliable, high-performance, and scalable data movement.
Develop and optimize PySpark-based data processing frameworks.
Build and manage Airflow workflows and orchestration pipelines.
Work with Hive and Kafka-based batch and streaming data solutions.
Implement data quality, monitoring, governance, and operational controls.
Support production incidents, performance tuning, and continuous improvement initiatives

Duties

• Lead the design and development of cloud-based data architectures utilizing GCP services such as BigQuery, Cloud Data Lake, and Cloud Storage to support scalable data warehousing and analytics projects.
• Develop comprehensive data models and system designs that align with business requirements, ensuring high performance, scalability, and security.
• Architect and implement ETL (Extract, Transform, Load) pipelines using tools like Informatica, Spark, and custom Java or Python scripts to facilitate seamless data integration across diverse sources including NoSQL databases like Cassandra or Bigtable.
• Collaborate with cross-functional teams in Agile environments to deliver robust data solutions that meet evolving business needs while maintaining best practices in system design for system development.
• Optimize database systems such as Microsoft SQL Server, Oracle, and cloud-native databases like Azure Data Lake for performance tuning and scalability.
• Drive master data management initiatives to ensure consistency, accuracy, and integrity of enterprise-wide data assets.
• Support big data processing frameworks like Hadoop and Spark for advanced analytics and data warehousing design projects.

Requirements

• Extensive experience in cloud database architecture with a focus on GCP services such as BigQuery, Cloud Data Lake, and related tools.
• Strong proficiency in SQL programming along with expertise in database design principles for relational and NoSQL systems.
• Proven track record of designing scalable ETL pipelines using tools like Informatica, Spark, Bash (Unix shell), Java, or Python.
• Deep understanding of data modeling concepts including linked data, master data management, and system architecture design for complex data environments.
• Hands-on experience with cloud infrastructure platforms such as AWS and Azure alongside GCP to support hybrid or multi-cloud architectures.
• Knowledge of big data technologies including Hadoop ecosystem components for large-scale data processing.
• Familiarity with business intelligence tools and analytics platforms to translate technical solutions into actionable insights.
• Excellent communication skills to collaborate effectively within Agile teams while translating technical requirements into clear architectural strategies.

Roles & Responsibilities:
The Data Engineer will be responsible for designing, building, governing, and optimizing data platforms, pipelines, and integrations to ensure secure, reliable, and scalable data availability for analytics, reporting, AI/ML, and business operations.
Preferred Experience
Strong Functional Knowledge in Retail (Search & Personalization) Added advantage.
Strong experience with large-scale GCP Data Engineering solutions.
Hands-on experience with Airflow DAG development and support
Experience building and optimizing Spark/PySpark data pipelines.
Exposure to Hive-based data processing and Kafka-driven ingestion frameworks.
Experience supporting analytics, personalization, recommendation, or search platforms is highly desirable

Pay: $80,000.00 - $110,000.00 per year

Work Location: Remote