Lead Data Engineer (Google Cloud Platform, Supply Chain & AI Data Platforms)

We are seeking an experienced and hands-on Lead Data Engineer to join our Enterprise Data & AI Organization. In this role, you will lead the design, development, and delivery of enterprise data products and advanced analytics solutions supporting complex supply chain, sourcing, transportation, and warehouse management (WMS) domains.

This is a technical leadership role requiring deep expertise in building modern, cloud-native data platforms on Google Cloud Platform (Google Cloud Platform). You will collaborate closely with Product Managers, Solution Architects, Data Architects, and cross-functional engineering teams to build scalable, high-quality data pipelines that power advanced analytics and AI-driven decision-making. If you excel at designing high-performance data models and driving engineering excellence, we want to hear from you., Data Architecture & Pipeline Development Cloud-Native Solutions: Design, develop, and implement scalable data pipelines, architectures, and data products on Google Cloud Platform (Google Cloud Platform). Modern Tooling: Build and optimize enterprise data solutions utilizing BigQuery, Dataproc, SQL, and dbt. ETL/ELT Engineering: Develop efficient, robust data ingestion and transformation pipelines to integrate data across multiple enterprise operational systems.

Data Modeling & Performance Optimization Dimensional Modeling: Design scalable data models supporting complex analytical, operational reporting, and AI requirements. Performance Tuning: Optimize data processing performance, reliability, scalability, and cost efficiency across cloud data platforms. Operational Reliability: Implement rigorous monitoring, automated testing, and operational best practices to support critical production workloads.

Technical Leadership & Collaboration Cross-Functional Partnership: Work alongside Product Managers, Business Analysts, and Enterprise Architects to translate business requirements into robust technical specifications. Code Quality: Lead technical design discussions, drive code reviews, and establish reusable engineering standards and documentation. Team Enablement: Participate actively in Agile delivery ceremonies, contribute to backlog refinement, and mentor junior data engineering team members., AllSTEM Connections participates in the E-Verify program in certain locations as required by law. Learn more about the E-Verify program. _Participation_Poster_ES.pdf

We also consider for employment qualified applicants regardless of criminal histories, consistent with legal requirements, including, if applicable, the City of Los Angeles’ Fair Chance Initiative for Hiring Ordinance. Pursuant to applicable state and municipal Fair Chance Laws and Ordinances, we will consider for employment-qualified applicants with arrest and conviction records, including, if applicable, the San Francisco Fair Chance Ordinance. For Los Angeles, CA applicants: Qualified applications with arrest or conviction records will be considered for employment in accordance with the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Pursuant to the San Francisco Fair Chance Ordinance, we will consider for employment qualified applicants with arrest and conviction records.

Experience Baseline: 8+ years of professional experience in Data Engineering, featuring demonstrated technical leadership on complex enterprise data projects. Cloud Mastery: Strong, hands-on production experience designing and deploying data solutions on Google Cloud Platform (Google Cloud Platform). Technical Proficiency (Expert Level): oDataproc & BigQuery oSQL & dbt (Data Build Tool) oModern ETL/ELT architecture principles and large-scale data processing techniques. Engineering Best Practices: Proven experience with relational/dimensional data modeling, Git version control, and CI/CD automated deployment pipelines. Core Competencies: Excellent verbal and written communication skills; strong analytical troubleshooting and problem-solving abilities.

Preferred Attributes Experience with workflow orchestration frameworks (e.g., Apache Airflow) and streaming data platforms (e.g., Apache Kafka). Working knowledge of PySpark for distributed data processing and Python for automation. Domain experience within retail, apparel, supply chain data platforms, logistics, transportation, or Warehouse Management Systems (WMS). Familiarity with data governance, data quality frameworks, and enterprise metadata management.