Job opening - ENG&JPN Bilingual Lead Data Engineer@Global FMCG Company in 東京23区 | Randstad

summary

東京23区, 東京都
¥10,000,000 - ¥13,000,000 per year, 年収1,000 ～ 1,300万円
permanent

job category
advertising, marketing & public relations

job details

社名
社名非公開

職種
アクセス解析、統計解析、データ分析、データアナリスト

業務内容 ...

・Strong ML Data Engineering Experience5+ years in data engineering, including 2+ years supporting ML workflows, with proven experience building scalable solutions for model development and deployment.・Expertise in Modern Data PlatformsHands-on experience with Databricks, Snowflake, Python, PySpark, and advanced SQL for developing and optimizing data pipelines and feature engineering processes.・MLOps and DevOps ProficiencyExperience with MLflow, Git, Docker, CI/CD pipelines, data versioning, model retraining, inference orchestration, and automated deployment practices.・Data Governance and Analytics CollaborationKnowledge of data governance, observability, and compliance principles, with experience partnering across data science and analytics teams and exposure to Power BI for validation and reporting.#LI-Hybrid#LI-KN

求められる経験
・Build and Maintain ML Data Pipelines
Design, develop, and optimize scalable data pipelines using Databricks, PySpark, Python, and Snowflake to support ML model training, inference, and feature engineering workflows.
・Develop Feature Store and MLOps Solutions
Create and manage reusable feature stores, implement CI/CD pipelines, automate model deployment, monitoring, retraining, and ensure reliable production ML operations.
・Provide Technical Leadership and Engineering Excellence
Lead architecture reviews, establish coding and data engineering standards, drive reusable framework development, and mentor team members on best practices.
・Ensure Cross-Functional Delivery and Production Reliability
Collaborate with Data Science, Analytics, IT, and DevOps teams to deliver business solutions, maintain agile delivery processes, implement data observability, and ensure SLAs for data and model availability.

保険
健康保険　厚生年金保険　雇用保険

休日休暇
土曜日　日曜日　祝日

給与
年収1,000 ～ 1,300万円

賞与
Yes
雇用期間
期間の定めなし

show more

社名
社名非公開

職種
アクセス解析、統計解析、データ分析、データアナリスト

業務内容
・Strong ML Data Engineering Experience5+ years in data engineering, including 2+ years supporting ML workflows, with proven experience building scalable solutions for model development and deployment.・Expertise in Modern Data PlatformsHands-on experience with Databricks, Snowflake, Python, PySpark, and advanced SQL for developing and optimizing data pipelines and feature engineering processes.・MLOps and DevOps ProficiencyExperience with MLflow, Git, Docker, CI/CD pipelines, data versioning, model retraining, inference orchestration, and automated deployment practices.・Data Governance and Analytics CollaborationKnowledge of data governance, observability, and compliance principles, with experience partnering across data science and analytics teams and exposure to Power BI for validation and reporting.#LI-Hybrid#LI-KN

求められる経験
・Build and Maintain ML Data Pipelines
Design, develop, and optimize scalable data pipelines using Databricks, PySpark, Python, and Snowflake to support ML model training, inference, and feature engineering workflows.
・Develop Feature Store and MLOps Solutions ...

Create and manage reusable feature stores, implement CI/CD pipelines, automate model deployment, monitoring, retraining, and ensure reliable production ML operations.
・Provide Technical Leadership and Engineering Excellence
Lead architecture reviews, establish coding and data engineering standards, drive reusable framework development, and mentor team members on best practices.
・Ensure Cross-Functional Delivery and Production Reliability
Collaborate with Data Science, Analytics, IT, and DevOps teams to deliver business solutions, maintain agile delivery processes, implement data observability, and ensure SLAs for data and model availability.

保険
健康保険　厚生年金保険　雇用保険

休日休暇
土曜日　日曜日　祝日

給与
年収1,000 ～ 1,300万円

賞与
Yes
雇用期間
期間の定めなし

show more

experience
・Build and Maintain ML Data Pipelines Design, develop, and optimize scalable data pipelines using Da

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