GCP Data Engineer

Position Overview

Our client is seeking a GCP Data Engineer to design, build, and optimize scalable data pipelines and cloud-native data platforms using Google Cloud Platform (GCP).

In this role, you will collaborate with Data Architects, Analysts, Data Scientists, and business stakeholders to deliver high-quality, reliable, and secure data solutions. These solutions will support enterprise analytics, digital transformation, and machine learning initiatives.

Key Responsibilities

Data Engineering & Cloud Development

• Design, build, and maintain large-scale, high-performance data pipelines using GCP services, including BigQuery, Cloud Storage, Dataflow, Dataproc, Pub/Sub, Cloud Composer, and Cloud Run.
• Develop ETL/ELT workflows using Apache Beam, Spark, or Python.
• Implement cloud-native data architectures, including data lakes, data marts, and enterprise data warehouses.
• Optimize data models for performance, cost efficiency, and scalability.

Data Integration & Automation

• Integrate data from a wide variety of internal and external sources using both batch and streaming processes.
• Automate data quality checks, schema validation, and data lineage tracking.
• Build reusable frameworks and templates to accelerate pipeline development.

Collaboration & Support

• Partner with application teams, product owners, and analysts to understand business data needs.
• Support Data Scientists by enabling feature pipelines, machine learning datasets, and efficient training data flows.
• Troubleshoot production data pipeline issues and implement long-term improvements.

Required Qualifications

• Bachelor's degree in Computer Science, Information Systems, Engineering, or a related field.
• 3–6 years of hands-on experience as a Data Engineer or in a similar role.
• Strong expertise in Google Cloud Platform (GCP), particularly BigQuery, Dataflow, Pub/Sub, and Cloud Storage.
• Proficiency in Python, SQL, and one or more frameworks such as Apache Beam or Spark.
• Experience with Airflow/Cloud Composer orchestration workflows.
• Solid understanding of data modeling, distributed systems, and ETL/ELT best practices.
• Familiarity with CI/CD, Terraform, Git, or related DevOps tools.

Preferred Qualifications

• Google Cloud Professional Data Engineer certification.
• Experience with streaming platforms such as Kafka or Pub/Sub.
• Knowledge of machine learning pipelines using Vertex AI or similar platforms.
• Experience working in enterprise retail or large-scale operational data environments.
• Familiarity with data governance tools such as Data Catalog, Collibra, or Alation.