Data Engineer

Job Description

Job Description

Job Title: Data Engineer

Location: Remote - PST time zone

Duration: Contract - 17 months

Job Description

We are seeking a Data Engineer to join our Supply Chain Data & AI organization. The ideal candidate will have strong experience in building cloud-native data products and pipelines on Google Cloud Platform using BigQuery, Dataproc, SQL, and dbt and a proven ability to deliver scalable, reliable data models and pipelines that power analytics and AI across Sourcing, Transportation, and Warehouse Management .

Responsibilities:

• Design, develop, and maintain scalable ETL/ELT pipelines on Google Cloud Platform.
• Build and optimize data solutions using Dataproc, BigQuery, SQL, and dbt.
• Develop and maintain high-quality data models for reporting, analytics, and AI.
• Ingest, transform, validate, and publish data from multiple enterprise source systems.
• Collaborate with product managers, business analysts, architects, and engineers to deliver scalable data solutions.
• Create reusable transformation components and follow engineering standards and best practices.
• Optimize data processing performance, reliability, and cost efficiency.
• Perform unit testing, data validation, and production support to ensure quality and stability.
• Participate in Agile ceremonies including sprint planning, backlog refinement, and code reviews.
• Document technical designs, data flows, and implementation details.

Required Skills & Qualifications:

• Bachelor’s degree in Computer Science, Engineering, Information Systems, or related field, or equivalent experience.
• 4+ years of experience in data engineering.
• Hands-on experience delivering data solutions on Google Cloud Platform.
• Proficiency with Dataproc, BigQuery, SQL, and dbt.
• Strong knowledge of data modeling, including dimensional modeling and analytical warehousing concepts.
• Experience building scalable ETL/ELT pipelines and processing large datasets.
• Familiarity with Git-based version control and CI/CD practices.
• Analytical, troubleshooting, and problem-solving skills.
• Effective communication and collaboration abilities.
• Apache Airflow for workflow orchestration.
• Apache Kafka or other event streaming platforms.
• PySpark for distributed data processing.
• Python for data engineering, scripting, and automation.
• Understanding of data quality, metadata management, and data governance.

Domain Experience (Highly Desirable):

• Retail industry, especially apparel.
• Supply chain data platforms.
• Transportation and logistics.
• Warehouse Management Systems (WMS).
• Distribution center operations.