Lead Data Engineer (GCP, Supply Chain & AI Data Platforms)

TL;DR

Lead Data Engineer (GCP, Supply Chain & AI Data Platforms): Building scalable cloud-native data platforms, enterprise data products, and advanced analytics pipelines on Google Cloud Platform with an accent on BigQuery, Dataproc, dimensional modeling, and reliable ETL/ELT architecture. Focus on optimizing performance and cost, designing production-grade data models, implementing monitoring and automated testing, and leading technical delivery across complex supply chain and warehouse management domains.

Location: San Francisco, California, United States

Salary: $120,600–$133,100 per year

Company

A staffing and workforce solutions company providing technical professionals for enterprise assignments.

What you will do

• Design, develop, and deliver scalable data pipelines, architectures, and enterprise data products on Google Cloud Platform.
• Build and optimize data solutions using BigQuery, Dataproc, SQL, and dbt.
• Develop robust ingestion and transformation pipelines integrating multiple enterprise operational systems.
• Design dimensional data models for analytics, operational reporting, and AI use cases.
• Improve processing performance, reliability, scalability, and cloud cost efficiency.
• Lead technical design discussions, code reviews, engineering standards, Agile delivery, and mentoring for junior data engineers.

Requirements

• 8+ years of professional data engineering experience, including technical leadership on complex enterprise projects.
• Hands-on production experience designing and deploying solutions on Google Cloud Platform.
• Expertise with Dataproc, BigQuery, SQL, dbt, modern ETL/ELT architecture, and large-scale data processing.
• Experience with relational and dimensional data modeling, Git, CI/CD, monitoring, and automated testing.
• Strong written and verbal communication, analytical troubleshooting, and problem-solving skills.
• Work location: San Francisco, United States.

Nice to have

• Experience with Apache Airflow and Apache Kafka.
• Working knowledge of PySpark and Python automation.
• Experience in retail, apparel, supply chain, logistics, transportation, or Warehouse Management Systems.
• Familiarity with data governance, data quality frameworks, and enterprise metadata management.

Culture & Benefits

• Collaboration with Product Managers, Solution Architects, Data Architects, Business Analysts, and engineering teams.
• Participation in Agile delivery ceremonies and backlog refinement.
• For temporary assignments lasting 13 weeks or longer: medical, dental, vision, 401(k), and statutory sick pay where required.
• Reasonable accommodations are available throughout the employment process.