Lead Data Engineer : 26-02168

The ideal candidate will have deep expertise in Google Cloud Platform (GCP), enterprise data engineering, and scalable ETL/ELT solutions, along with experience supporting Supply Chain, Transportation, Sourcing, and Warehouse Management (WMS) domains. We are seeking an experienced Lead Data Engineer – Data & AI, Supply Chain to join a high-performing Data & AI organization focused on building modern, cloud-native data platforms that power enterprise analytics and AI initiatives.

Primary Skills: GCP Data Engineering (Expert), BigQuery, Dataproc & dbt (Expert), ETL/ELT Pipeline Development (Expert), Data Modeling & SQL (Advanced), Supply Chain Data Engineering (Advanced)
Contract Type: W2 Only
Duration: 6+ Months
Location: San Francisco, CA
Pay Range: $65 - $68 on W2

Job Summary:
We are seeking an experienced Lead Data Engineer – Data & AI, Supply Chain to join a high-performing Data & AI organization focused on building modern, cloud-native data platforms that power enterprise analytics and AI initiatives. The ideal candidate will have deep expertise in Google Cloud Platform (GCP), enterprise data engineering, and scalable ETL/ELT solutions, along with experience supporting Supply Chain, Transportation, Sourcing, and Warehouse Management (WMS) domains. This is a hands-on technical leadership role responsible for designing enterprise data products, mentoring engineering teams, and delivering high-quality cloud-based analytics solutions.
Key Responsibilities:

• Design, develop, and implement scalable enterprise data pipelines and data products on Google Cloud Platform (GCP).
• Build and optimize cloud-native data solutions using BigQuery, Dataproc, SQL, and dbt.
• Develop scalable ETL/ELT pipelines to ingest, transform, and publish data from multiple enterprise systems.
• Design robust data models supporting enterprise reporting, analytics, and AI-driven decision-making.
• Collaborate with Product Managers, Business Analysts, Solution Architects, Data Architects, and business stakeholders to translate business requirements into technical solutions.
• Lead technical design discussions, architecture reviews, and code reviews while promoting engineering best practices.
• Optimize cloud data platforms for performance, scalability, reliability, and cost efficiency.
• Implement monitoring, testing, CI/CD, and operational best practices for production data pipelines.
• Develop reusable frameworks, engineering standards, and technical documentation to improve team productivity.
• Troubleshoot production issues, support continuous improvement initiatives, and mentor junior engineers.
• Participate in Agile ceremonies including sprint planning, backlog refinement, estimation, and technical planning.

Must-have Skills:

• 8+ years of Data Engineering experience with demonstrated technical leadership on enterprise-scale projects.
• Strong hands-on experience with Google Cloud Platform (GCP).
• Expert-level experience with BigQuery, Dataproc, SQL, and dbt.
• Strong knowledge of modern ETL/ELT architecture and large-scale cloud data processing.
• Expertise in data modeling, including dimensional modeling, normalized models, and analytical data warehouse design.
• Experience building scalable, maintainable cloud-native data pipelines.
• Strong experience with Git, CI/CD pipelines, and software engineering best practices.
• Excellent analytical, troubleshooting, and problem-solving skills.
• Strong communication and collaboration skills with cross-functional technical and business teams.

Nice-to-have Skills:

• Experience with Apache Airflow for workflow orchestration.
• Experience integrating enterprise data platforms using Apache Kafka or other streaming technologies.
• Working knowledge of PySpark for distributed data processing.
• Proficiency in Python for automation, utilities, and data engineering.
• Experience implementing data quality frameworks, metadata management, and data governance best practices.
• Experience supporting AI/ML data platforms and enterprise analytics initiatives.

Preferred Qualifications:

• Bachelor's degree in Computer Science, Information Systems, Engineering, Data Science, or a related field.
• Experience within Retail, Apparel, Supply Chain, Transportation, Logistics, Warehouse Management Systems (WMS), or Distribution Center Operations.
• Proven experience leading technical teams and mentoring engineers in Agile environments.
• Strong understanding of enterprise data architecture, cloud-native engineering, and modern analytics platforms.
• Passion for building scalable, reusable, and high-performance data solutions that enable enterprise analytics and AI capabilities.

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