{"schemaVersion":"jobsearcher.job.v1","id":"0f2d2673dd595b711fde6a8c","url":"https://jobsearcher.com/jobs/0f2d2673dd595b711fde6a8c","canonicalUrl":"https://jobsearcher.com/jobs/0f2d2673dd595b711fde6a8c","title":"Lead Data Engineer :","description":"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. #J-18808-Ljbffr","company":"Akraya","rawCompany":"akraya","city":"Millbrae","state":"CA","isRemote":false,"isActive":false,"createdAt":"2026-08-04T04:24:48.566Z","occupations":[{"code":"15-1243.01","title":"Data Warehousing Specialists","slug":"data-warehousing-specialists"},{"code":"15-2051.00","title":"Data Scientists","slug":"data-scientists"},{"code":"15-1252.00","title":"Software Developers","slug":"software-developers"}],"industries":[{"code":"541512","title":"Computer Systems Design Services","slug":"computer-systems-design-services"},{"code":"513210","title":"Software Publishers","slug":"software-publishers"},{"code":"541511","title":"Custom Computer Programming Services","slug":"custom-computer-programming-services"}],"jobPosting":{"@context":"https://schema.org","@type":"JobPosting","title":"Lead Data Engineer :","description":"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. #J-18808-Ljbffr","datePosted":"2026-08-04T04:24:48.566Z","dateModified":"2026-08-04T04:24:48.566Z","hiringOrganization":{"@type":"Organization","name":"Akraya","sameAs":"https://jobsearcher.com"},"jobLocation":{"@type":"Place","address":{"@type":"PostalAddress","addressLocality":"Millbrae","addressRegion":"CA","addressCountry":"US"}},"identifier":{"@type":"PropertyValue","name":"JobSearcher","value":"0f2d2673dd595b711fde6a8c"},"url":"https://jobsearcher.com/jobs/0f2d2673dd595b711fde6a8c"}}