A logo

Lead Data Engineer Data & AI, Supply Chain : 26-02170

Akraya Inc.San Francisco, CA

$65 - $68 / hour

Automate your job search with Sonara.

Submit 10x as many applications with less effort than one manual application.1

Reclaim your time by letting our AI handle the grunt work of job searching.

We continuously scan millions of openings to find your top matches.

pay-wall

Overview

Remote
On-site
Compensation
$65-$68/hour

Job Description

Primary Skills: Google Cloud Platform (GCP) (Expert), BigQuery & Dataproc (Expert), ETL/ELT & Data Pipeline Development (Expert), SQL & dbt (Expert), Data Modeling & Cloud Data Warehousing (Advanced)Contract Type: W2 OnlyDuration: 6+ MonthsLocation: San Francisco, CAPay Range: $65 - $68 on W2Job Summary: We are seeking an experienced Lead Data Engineer – Data & AI, Supply Chain to design, develop, and deliver scalable cloud-native data platforms supporting Supply Chain analytics and AI initiatives. The ideal candidate will have deep expertise in Google Cloud Platform (GCP), BigQuery, Dataproc, SQL, and dbt, with proven experience building enterprise-scale ETL/ELT pipelines, data models, and data products. This is a hands-on technical leadership role requiring close collaboration with Product Managers, Data Architects, Solution Architects, and business stakeholders to deliver high-performance data solutions across Sourcing, Transportation, and Warehouse Management (WMS).Key Responsibilities:
  • Design, develop, and maintain scalable ETL/ELT pipelines and enterprise data products on Google Cloud Platform (GCP).
  • Build and optimize cloud-native data solutions using BigQuery, Dataproc, SQL, and dbt.
  • Design robust dimensional, normalized, and analytical data models to support reporting, analytics, and AI use cases.
  • Develop scalable data ingestion, transformation, validation, and publishing pipelines from multiple enterprise source systems.
  • Collaborate with Product Managers, Business Analysts, Data Architects, and Solution Architects to translate business requirements into technical solutions.
  • Lead technical design discussions, perform code reviews, and establish engineering best practices.
  • Optimize data processing performance, scalability, reliability, and cloud cost efficiency.
  • Implement monitoring, testing, CI/CD, and operational best practices for production data workloads.
  • Develop reusable frameworks, engineering standards, and technical documentation.
  • Troubleshoot production issues and drive continuous platform improvements.
  • Mentor engineers and actively participate in Agile ceremonies, sprint planning, backlog refinement, and estimation.
Must-have Skills:
  • 8+ years of Data Engineering experience with technical leadership on enterprise data platforms.
  • Strong hands-on experience with Google Cloud Platform (GCP).
  • Expert experience with BigQuery, Dataproc, SQL, and dbt.
  • Strong experience designing and developing scalable ETL/ELT pipelines.
  • Expertise in dimensional modeling, normalized data models, and cloud data warehouse architecture.
  • Experience building cloud-native, scalable, and maintainable data pipelines.
  • Experience with Git, CI/CD pipelines, and software engineering best practices.
  • Excellent analytical, troubleshooting, and performance optimization skills.
  • Strong communication skills with the ability to collaborate across technical and business teams.
Nice-to-have Skills:
  • Apache Airflow workflow orchestration.
  • Apache Kafka or other event streaming technologies.
  • PySpark for distributed data processing.
  • Python for data engineering automation and utilities.
  • Data quality, metadata management, and data governance.
  • Retail, Supply Chain, Transportation, Logistics, Warehouse Management Systems (WMS), or Distribution Center domain experience.
Preferred Qualifications:
  • Bachelor's degree in Computer Science, Engineering, Information Systems, or related field.
  • Experience delivering cloud-native data platforms supporting enterprise analytics and AI initiatives.
  • Proven leadership in mentoring engineers and driving engineering best practices.
  • Experience working within Agile software development environments.
  • Strong understanding of Supply Chain, Retail, Transportation, or Warehouse Management business processes.
ABOUT AKRAYA Akraya is an award-winning IT staffing firm consistently recognized for our commitment to excellence and a thriving work environmentMost recently, we were recognized Stevie Employer of the Year 2025, SIA Best Staffing Firm to work for 2025, Inc 5000 Best Workspaces in US (2025 & 2024) and Glassdoor's Best Places to Work (2023 & 2022)!Industry Leaders in Tech Staffing As Talent solutions provider for Fortune 100 Organizations, Akraya's industry recognitions solidify our leadership position in the IT staffing space. We don't just connect you with great jobs, we connect you with a workplace that inspires!Join Akraya Today! Let us lead you to your dream career and experience the Akraya difference. Browse our open positions and join our team!

Automate your job search with Sonara.

Submit 10x as many applications with less effort than one manual application.

pay-wall

FAQs About Lead Data Engineer Data & AI, Supply Chain : 26-02170 Jobs at Akraya Inc.

What is the work location for this position at Akraya Inc.?
This job at Akraya Inc. is located in San Francisco, CA, according to the details provided by the employer. Some roles may also include multiple work locations depending on the requirement.
What pay range can candidates expect for this role at Akraya Inc.?
Candidates can expect a pay range of $65–$68 per hour for this role.
What employment applies to this position at Akraya Inc.?
The employer has not provided this information. This may be discussed during the hiring process.
What is the process to apply for this position at Akraya Inc.?
You can apply for this role at Akraya Inc. either through Sonara's automated application system, which helps you submit applications 10X faster with minimal effort, or by applying manually using the direct link on the job page.