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Coca-Cola

Lead Data Engineer

Coca-Cola is hiring a Lead Data Engineer in Tokyo, Japan. Level rates it ; you can apply on Coca-Cola.

Level's read on this job

  • On Level's scale, this data engineer role is Uses AI ●●○○: an ordinary role that requires AI tools.
  • The posting mentions data engineering, etl and data pipelines among its tools and skills.
  • Level classes it as a lead role.
  • Coca-Cola has 253 other open roles on Level.

AI in this role

Lead the data engineering function for Japan and South Korea, building scalable enterprise data platforms and automated pipelines.

data-engineeringetldata-pipelinesci-cdcloud-platforms

Job Description Summary:

Lead Data Engineer (Director)

Team: OU Digital, Japan & South Korea Operating Unit

Reports To: Senior Director, Data & Analytics

The Coca-Cola Company (TCCC) is evolving into a more digital-first, data-driven enterprise to fuel sustainable growth, increase speed to market, and unlock new sources of value. Rapid advances in technology, data, and AI create a unique opportunity to accelerate The Coca-Cola Company system's next chapter of growth by reimagining our core competitive advantages. Building on a strong foundation of modernized infrastructure, global digital experiences, early data capabilities, and proven use cases, we are now shifting from early wins to scaled, end-to-end impact on growth, productivity, and efficiency.

This next phase moves us from an analog world to a fully digitized one, powered by a digital growth engine that embeds data, technology, and AI into our most critical business domains. Success will require more than technology implementation — it demands a strategic transformation of core capabilities, a domain-based, product-centric operating model, and a cultural shift toward experimentation, data and technology fluency, and seamless partnership between business and digital teams.

Job Description Summary

The Lead Data Engineer owns the data engineering function for the Japan & South Korea Operating Unit, leading the design, delivery, and operation of enterprise data platforms and pipelines. The role translates data strategy into scalable engineering roadmaps, builds and leads a data engineering team, and represents data engineering in OU data governance forums.

As the engineering leader for JKOU's data foundation, the Director ensures platforms are reliable, automated, and engineered for scale — enabling trusted, high-quality data across the organization's most critical business domains.

Responsibilities include

  • Own and lead ETL and data pipeline engineering for OU data products and platforms, setting technical direction and engineering standards.

  • Translate the data strategy into scalable engineering roadmaps, technical designs, and multi-year platform plans.

  • Design, build, and operate enterprise data platforms in alignment with global architecture standards, ensuring scalability and resilience.

  • Automate data pipelines and operations to maximize reliability and minimize manual effort.

  • Ensure CI/CD reliability, platform performance, and engineering excellence across the data estate.

  • Integrate local and system data sources, including bottler and partner data, into governed platforms.

  • Partner with data science, governance, and product management to deliver end-to-end data products.

  • Manage vendor engineering resources and partnerships, ensuring quality, cost-effectiveness, and delivery accountability.

  • Build, lead, and develop a data engineering team, fostering technical excellence and career growth.

  • Represent data engineering in OU data governance forums, advocating for platform standards, data quality, and architectural integrity.

Organizational Impact

  • Owns the engineered data foundation for OU analytics, AI, and data products, directly determining their reliability and scalability.

  • Leads a data engineering team and vendor partners, shaping engineering talent and capability for the OU.

  • Represents data engineering in governance and architecture forums, influencing OU-wide data standards.

Innovation & Complexity

  • Sets the technical direction for modern data engineering practices — automation, CI/CD, infrastructure-as-code — to raise platform excellence across the OU.

  • Solves complex, ambiguous data integration challenges across system, bottler, and partner sources, often without established precedent.

  • Balances near-term delivery pressure with long-term architectural sustainability.

Communication & Influence

  • Communicates engineering strategy, trade-offs, and platform health to senior data and business leadership.

  • Coordinates across global platform teams, local stakeholders, and vendors to align priorities and resolve conflicts.

  • Represents data engineering in OU governance forums, influencing standards adopted across teams.

Candidate Profile

A senior data engineering leader with a track record of building and leading engineering teams, architecting platforms at scale, and translating strategy into reliable, automated systems. Combines deep technical expertise with the leadership and influence needed to represent data engineering in cross-functional governance.

Experience & Qualifications

  • 10+ years of experience in data engineering, including ETL/ELT, data platforms, and pipeline operations, with demonstrated progression into leadership.

  • Proven experience building and leading data engineering teams.

  • Strong proficiency in modern data stacks (cloud data platforms, orchestration, CI/CD).

  • Experience designing scalable data architectures aligned with enterprise standards.

  • Experience representing engineering functions in governance or architecture review forums.

  • Experience with vendor management and Agile delivery.

  • Business-level English and Japanese required.

Skills:

Location(s):

Japan

City/Cities:

Tokyo

Travel Required:

00% - 25%

Relocation Provided:

No

Job Posting End Date:

November 30, 2026

Our Purpose and Growth Culture:

We are taking deliberate action to nurture an inclusive culture that is grounded in our company purpose, to refresh the world and make a difference. We act with a growth mindset, take an expansive approach to what’s possible and believe in continuous learning to improve our business and ourselves. We focus on four key behaviors – curious, empowered, inclusive and agile – and value how we work as much as what we achieve. We believe that our culture is one of the reasons our company continues to thrive after 130+ years. Visit Our Purpose and Vision to learn more about these behaviors and how you can bring them to life in your next role at Coca-Cola.

How we rate this

Lead Data Engineer at Coca-Cola rates 40 out of 100 for how much of the daily work is AI. That makes it Uses AI (AI Level 2 of 4). The level is about AI in the job, not seniority.

Classification

Uses AI. An ordinary role that requires AI tools.

  1. ●●●● Builds AI80 to 100
  2. ●●●○ Works on AI60 to 79
  3. ●●○○ Uses AI40 to 59
  4. ●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

Data EngineeringETLData PipelinesCI/CDCloud Platforms

Also common in data engineer jobs at this level

Pay

Typical data engineer job
$146k

This posting doesn't list pay. Use the typical data engineer figure when pay comes up. See data engineer jobs

Questions you could be asked

  1. Tell me about a project where data engineering was part of your work. What did you do?
  2. Tell me about a project where etl was part of your work. What did you do?
  3. Tell me about a project where data pipelines was part of your work. What did you do?
  4. Tell me about a project where ci/cd was part of your work. What did you do?
  5. Tell me about a project where cloud platforms was part of your work. What did you do?

Adapt your resume

  • List these exact terms on your resume: Data Engineering, ETL, Data Pipelines, CI/CD, and Cloud Platforms. An applicant tracking system matches the wording, not the idea.
  • Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
  • Put the AI tool in a bullet point about what you did, not just in a skills list. This role treats it as a required part of the job.

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