Data Engineer

We are seeking a skilled and driven Data Engineer to join our enterprise data and analytics organization. In this role, you will focus on developing and supporting scalable data products and robust ETL/ELT pipelines that power advanced analytics and AI initiatives across sourcing, transportation, and warehouse management domains.

This is a hands-on technical position where you will collaborate closely with product managers, data architects, business analysts, and fellow engineers to design, build, test, and maintain cloud-native data solutions. Utilizing modern Google Cloud Platform (Google Cloud Platform) technologies, you will transform enterprise data into trusted, high-quality assets that drive operational excellence. If you have a strong background in building scalable data pipelines on Google Cloud Platform, we want to hear from you., Data Pipeline & Infrastructure Development ETL/ELT Architecture: Design, develop, test, and maintain scalable ETL/ELT data pipelines on Google Cloud Platform (Google Cloud Platform) to ingest, transform, validate, and publish multi-source enterprise data. Cloud Optimization: Build and optimize high-performance data solutions leveraging Dataproc, BigQuery, advanced SQL, and dbt. Data Modeling: Develop and maintain rigorous data models (including dimensional modeling and analytical warehouse concepts) supporting reporting and AI use cases.

Collaboration & Engineering Standards Cross-Functional Delivery: Partner with product managers, business analysts, and architects to translate business requirements into effective technical data solutions. Performance & Cost Efficiency: Continuously optimize data processing performance, system reliability, and cloud resource cost-efficiency. Quality Assurance: Perform unit testing, automated data validation, and production support to ensure flawless data quality and operational stability.

Agile Delivery & Documentation Agile Ceremonies: Participate actively in sprint planning, backlog refinement, code reviews, and cross-functional team ceremonies. Technical Documentation: Document technical designs, data flows, and implementation details to ensure maintainability and knowledge sharing., 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.

Education: Bachelor’s degree in Computer Science, Engineering, Information Systems, or a related quantitative field, or equivalent practical experience. Experience Baseline: 4+ years of professional experience in Data Engineering. Technical Mastery: oHands-on experience building production data solutions on Google Cloud Platform (Google Cloud Platform). oStrong proficiency with Dataproc, BigQuery, advanced SQL, and dbt (Data Build Tool). oDeep understanding of data modeling techniques, dimensional modeling, and analytical data warehouse architecture. oProven experience building scalable ETL/ELT pipelines for large datasets. oWorking knowledge of Git-based version control and CI/CD best practices. Core Competencies: Exceptional analytical and troubleshooting abilities; strong communication skills; ability to work independently within fast-paced Agile teams.

Preferred Attributes Experience with workflow orchestration tools such as Apache Airflow. Familiarity with event streaming platforms (e.g., Apache Kafka) and distributed data processing using PySpark and Python. Domain expertise within retail, apparel, supply chain platforms, transportation logistics, or Warehouse Management Systems (WMS).