Lead Data Engineer – Data & AI, Supply Chain

Hi,

Please review the below job requirement and let me know if you are good to submit with the below details filled and your latest resume ASAP.

Lead Data Engineer – Data & AI, Supply Chain

Location: Pleasanton CA

Duration – 12 months

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

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.

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

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