Senior Data Engineer - Supply Chain
Pleasanton, CA
Hybrid
The ideal candidate is a hands-on technical leader with deep expertise in building modern cloud-native data platforms on Google Cloud Platform (Google Cloud Platform). 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.
Key Responsibilities
• Design, develop, and implement scalable data pipelines and data products on Google Cloud Platform (Google Cloud Platform).
• 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
Required Technical Skills
• 8+ years of experience in Data Engineering with demonstrated technical leadership on enterprise data projects.
• Strong hands-on experience with Google Cloud Platform (Google Cloud Platform).
• Expert-level proficiency in:
o Dataproc
o BigQuery
o SQL
o 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 Technical 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.
Domain Experience (Highly Desirable)
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.
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