Lead Data Engineer

Job Title: Lead Data Engineer – Data & AI, Supply Chain Location: San Francisco, CA 94105, USA (Onsite) Duration: 6 Months About the Role Client 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. 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 (GCP). • 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. Key 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 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.