応募資格

必須

Bachelor’s or Master’s degree in Computer Science, Data Engineering, Information Systems, or a related quantitative discipline.
6+ years in data engineering roles, with 2+ years focused on ML data workflows.
Proven experience enabling ML model development and deployment through scalable engineering solutions.
Hands-on experience with Snowflake and Databricks in production environments.
Experience contributing to cross-functional data science or analytics programs.
Experience designing feature stores and implementing feature pipelines in ML production environments.
Working knowledge of CI/CD, data versioning, and DevOps practices for pipeline and model automation.
Deep proficiency in Python and PySpark for data transformation and ML pipeline development in tools like Databricks.
Advanced SQL capabilities for data wrangling, transformation, and pipeline optimization.
Hands-on experience with MLflow, Git, Docker, and other DevOps tools.
Working knowledge of model retraining, inference orchestration, and version control.
Understanding of data governance, observability, and engineering compliance principles.
Exposure to BI tools like Power BI for quick validation and prototyping.
Demonstrated ability to lead through expertise and technical credibility.
Skilled in communicating complex architecture to business and non-technical partners.
Works cross-functionally with DS, Analytics, and IT to drive aligned delivery.
Embraces continuous improvement and knowledge-sharing mindset.
Comfortable mentoring junior team members without formal people management.
Driven by automation, reusability, and long-term scalability.
Curious and proactive about learning new technologies and industry trends.

求める人物像

Embraces continuous improvement and knowledge-sharing mindset.
Driven by automation, reusability, and long-term scalability.
Curious and proactive about learning new technologies and industry trends.

募集要項最終確認 10/5

職種: データエンジニア
雇用形態: 正社員3か月
役職: Lead ML Data EngineerM5
給与: 記載なし
給与の詳細: 非公開
手当: 休日手当
勤務地: 150-0002東京都渋谷区渋谷4-6-3 3F
働き方: 標準勤務時間 9:00～17:45フルフレックス、在宅勤務年間休日数121日。週休2日（土日休）会社カレンダーに準ずる
勤務時間: 標準勤務時間 9:00～17:45フルフレックス、在宅勤務年間休日数121日。週休2日（土日休）会社カレンダーに準ずる
年間休日: 121日
休日・休暇: 年間休日数121日週休2日（土日休）会社カレンダーに準ずる結婚休暇葬祭休暇公傷病休暇私傷病積立休暇公用休暇罹災休暇赴任休暇配偶者出産休暇リフレッシュ休暇（永年勤続表彰）子の看護休暇/介護休暇（年間5労働日）ならし保育休暇ととのえ休暇（生理休暇）出産休暇
福利厚生: 財産形成：退職金制度（企業型確定拠出年金 ※100%会社負担）従業員持株会グループ保険育児休業介護休業育児短時間勤務（子供が小学校3年生終了まで）チャレンジ休業制度；社員のキャリアアップ（学位や専門能力取得）を目的に6か月以上3年以下の休業を認める制度ウェルカムバック制度（再雇用制度）その他：共済会、健康支援、各地契約保養施設、各種割引券など
社会保険: 社会保険完備（健康保険、厚生年金保険、雇用保険、労災保険）
経験年数: 6年以上
学歴: 大学卒業以上
掲載日: 2026/10/04 11:59（2日前）
最終確認: 2026/10/05 22:02（1日前）

仕事内容

Role Purpose:

The Lead ML Data Engineer is a senior technical leader responsible for enabling scalable, production-grade Data Science & Analytics (DSA) solutions within Coca-Cola Bottlers Japan Inc.'s (CCBJI) Vending Machines (VM) business unit. This role leads the development, optimization, and management of end-to-end ML and Analytics data workflows, ensuring reliable and efficient infrastructure for AI solutions like Assortment, Column Reallocation, and Placement.
The role works in close partnership with Data Scientists, Analytics Specialists, and IT to deliver high-impact, ML-ready datasets via best of breed tools. The role plays a key function in bridging business objectives with technical delivery by converting commercial data requirements into robust pipelines, reusable assets, and operational tooling.
The position also drives quality standards and data engineering practices within the team, ensuring model reproducibility, pipeline traceability, and integration with enterprise MLOps and governance frameworks.

Advanced ML Data Pipeline Development

Design, develop, and maintain robust data pipelines to support ML model training, inference, and feature transformation workflows.
Deliver performant and modular pipelines using Databricks (PySpark/Python) and Snowflake, aligned to architectural best practices.
Ensure end-to-end ownership of data engineering from working with IT on raw data ingestion to developing model-ready feature layers.
Implement CI/CD-ready transformation logic for reproducibility and handover to downstream components.

Feature Stores & Reusability Framework

Architect and maintain a centralized, scalable feature stores that enables reuse across multiple DS use cases.
Define feature documentation standards, naming conventions, and lifecycle management practices.
Optimize joins, aggregations, and lookups to balance compute cost with accuracy and inference performance.

MLOps Integration & Model Lifecycle Engineering

Work closely with the Data Scientists and Analytics Specialists to operationalize models through CI/CD pipelines (MLflow, GitHub Actions, Databricks Workflows).
Design robust systems for retraining triggers, monitoring, and automated evaluation of production ML models.
Implement failure recovery, alerting, and model rollback procedures in collaboration with IT and DevOps.

Engineering Excellence & Domain Leadership

Serve as the go-to engineering authority for ML enablement within the DSA team.
Lead technical design reviews, set code standards, and promote reusability and modularization across pipelines.
Contribute internal tools, libraries, and utilities that improve engineering velocity and onboarding.
Conduct informal mentoring and coaching for junior engineers and scientists working with data pipelines.

Agile Program Delivery

Actively participate in agile sprint cycles, contributing to planning, estimation, retrospectives, and delivery metrics.
Align with the Analytics Portfolio Manager, DS Manager, and Analytics Manager to prioritize deliverables and resolve cross-functional dependencies.
Maintain and manage engineering backlog, surfacing technical debt or architectural decisions that require executive alignment.

Cross-Functional Collaboration

Translate ambiguous business requirements into structured, scalable data solutions that accelerate DS and Analytics outcomes.
Collaborate with the Analytics team to build curated views and pre-aggregated layers for Power BI or experimentation workflows.
Partner with IT to ensure infrastructure provisioning, access control, and platform governance align with CCBJI’s enterprise standards.

Data Observability & Production Assurance

Build and maintain monitoring systems to track pipeline performance, schema changes, and data freshness.
Implement quality checks and exception handling to reduce operational risk and manual rework.
Ensure SLAs are defined and met for model refreshes, data availability, and system uptime.

Key Outputs:

Stable, scalable ML data pipelines that serve predictive models across VM business scenarios.
Well-documented and reusable feature store logic shared across DS initiatives.
Fully operationalized MLOps workflows supporting model retraining and deployment.
Toolkits, templates, standards and frameworks adopted by team members for faster pipeline delivery.
Reduction in time-to-ML model deployment time and increased delivery velocity.
Measurable improvements in pipeline stability, data quality, and platform observability with operational dashboards

Performance Success Criteria (Examples):

Launch production-grade ML pipelines for at least three strategic models within first 9–12 months.
Reduce end-to-end model deployment cycle time by 25% through reusability and automation.
Deliver a reusable feature store structure adopted by at least 3 DS initiatives.
Implement and operationalize monitoring workflows covering pipeline reliability and data quality.
Create internal engineering utilities or templates reused by at least two other team members.
Maintain 98%+ reliability of ML workflows and data pipelines with documented support procedures.

この求人は、コカ・コーラボトラーズジャパン株式会社の採用ページの内容をAIが読み取って整理したものです。応募の前に採用ページの原文を確かめてください。データの集め方企業サイト

Lead ML Data Engineer in DSA, Vending Transformation Planning HQ

どんな仕事かAIによる生成

この求人に応募

応募資格

必須

求める人物像

募集要項最終確認 10/5

仕事内容

Role Purpose:

Advanced ML Data Pipeline Development

Feature Stores & Reusability Framework

MLOps Integration & Model Lifecycle Engineering

Engineering Excellence & Domain Leadership

Agile Program Delivery

Cross-Functional Collaboration

Data Observability & Production Assurance

Key Outputs:

Performance Success Criteria (Examples):

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