Data Engineer

Description: Lead Data Engineer – Data & AI, Supply Chain About the Role Tech 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. 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. 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: oDataproc oBigQuery oSQL odbt (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