Lead Data Engineer – Data & AI, Supply Chain

Location: Pleasanton, CA • Work Model: Fully Onsite M-F • Contract (W2 only, no corp-to-corp)

About the Role

Company is seeking an experienced Lead Data Engineer to join the Supply Chain Data & AI Organization. This role will support the design, development, and delivery of enterprise data products and analytics solutions across the Sourcing, Transportation, and Warehouse Management (WMS) domains. The ideal candidate is a hands-on technical leader with deep expertise in building modern cloud-native data platforms on Google Cloud Platform (GCP). You will collaborate with Product Managers, Solution Architects, Data Architects, Business SMEs, and engineering teams to develop scalable, high-quality data solutions that enable advanced analytics and AI-driven decision making.

Responsibilities

• Design, develop, and implement scalable data pipelines and data products on Google Cloud Platform (GCP).
• Build and optimize enterprise data solutions using Dataproc, BigQuery, SQL, and dbt.
• Design robust and scalable data models that support analytical and operational reporting requirements.
• Develop efficient ETL/ELT pipelines to ingest, transform, and publish data from multiple enterprise systems.
• Collaborate with Product Managers, Business Analysts, Enterprise Solution Architects, Data Architects, and business stakeholders to translate business requirements into scalable technical solutions.
• Lead technical design discussions and perform code reviews to ensure engineering quality and adherence to standards.
• Optimize data processing performance, reliability, scalability, and cost across cloud-based data platforms.
• Implement monitoring, testing, and operational best practices to support production workloads.
• Contribute to reusable frameworks, engineering standards, and documentation that improve team productivity and solution consistency.
• Support production issue resolution and continuous improvement initiatives.
• Work effectively within Agile delivery teams and participate in sprint planning, estimation, and backlog refinement.
• Mentor team members.

Requirements

• 6+ years of experience in Data Engineering with demonstrated technical leadership on enterprise data projects.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Expert-level proficiency in:
  • Dataproc
  • BigQuery
  • SQL
  • dbt (Data Build Tool)
• Strong understanding of modern ETL/ELT architecture and large-scale data processing.
• Strong knowledge of data modeling techniques, including dimensional modeling, normalized data models, and analytical data warehouse design.
• Experience building scalable and maintainable cloud-native data pipelines.
• Experience with Git, CI/CD pipelines, and engineering best practices.
• Strong analytical, troubleshooting, and problem-solving skills.
• Excellent verbal and written communication skills with the ability to collaborate effectively across cross-functional teams.

Preferred Skills

• Experience with Apache Airflow for workflow orchestration.
• Experience integrating enterprise data platforms with Apache Kafka or other streaming technologies.
• Working knowledge of PySpark for distributed data processing.
• Proficiency in Python for data engineering, automation, and utility development.
• Familiarity with data quality, metadata management, and data governance best practices.

Desired Domain Experience

Candidates with experience in one or more of the following areas will be strongly preferred:

• Retail industry (Apparel)
• Supply Chain data platforms
• Transportation and Logistics
• Warehouse Management Systems (WMS)
• Distribution Center operations

Desired Attributes

• Self-driven and able to work independently in a fast-paced environment.
• Strong ownership mindset with a focus on delivering high-quality solutions.
• Ability to balance technical excellence with business priorities.
• Effective collaborator who can work seamlessly with business partners, architects, product managers, and engineering teams.
• Passion for building scalable, reliable, and reusable data solutions that enable analytics and AI capabilities across the Supply Chain organization.