Data Engineer - Conversational Experiences Department (CXD)

掲載中・2日前に確認

給与: 記載なし
勤務地: 東京都
休日: 記載なし
雇用形態: 記載なし

どんな仕事かAIによる生成

This Data Engineer will build the data foundation for a V1 customer communication as a service (CCaaS) product. The role owns data infrastructure from ingestion through serving, including production ETL/ELT pipelines, cloud data warehouse datasets, and event-centric data models. The engineer will partner with Data Analysts and Product Managers and establish data quality and governance fundamentals.

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応募資格

必須

Over 5 years of hands-on experience in data engineering, including building and operating production data pipelines.
Advanced SQL and strong proficiency in at least one programming language such as Python or Go.
Experience with cloud data warehouses such as BigQuery, Snowflake, or Databricks, including performance tuning at scale.
Experience with ETL/ELT orchestration tools such as Apache Airflow, dbt, or Google Cloud Dataflow.
Experience designing analytics data models and data marts (e.g., dimensional modeling, dbt or similar) across layered architectures (medallion bronze/silver/gold), owning their quality and documentation.
Familiarity with streaming technologies such as Kafka or Pub/Sub for real-time ingestion.
Experience building data infrastructure from scratch (greenfield or V1 product context) rather than only maintaining existing platforms.
Experience partnering with analysts, product managers, or engineers to deliver reliable, reusable data solutions.
Fluent in English, with strong communication skills to collaborate effectively in a multinational environment.
Ability to design data solutions robust enough to absorb the ambiguity and changing requirements typical of V1 product development in a fast-paced, agile environment.

歓迎

Experience with cloud-native deployment: Docker, Kubernetes, and CI/CD for data workloads.
Familiarity with IaC tools such as Terraform for data infrastructure provisioning.
Experience with distributed data processing frameworks such as Apache Spark for large-scale data transformation.
Data quality or data governance tooling experience (e.g., dbt tests, Soda, Elementary, data catalogs).
Analyst skillset: ability to go beyond building pipelines and explore the data yourself, including exploratory data analysis, dashboard creation in BI tools such as Tableau, Looker, or DOMO, and supporting A/B test analysis. You can step into an analyst's shoes when the team needs it.
Experience in the CCaaS, communications technology, telecom, or AI-driven product space.

募集要項最終確認 11/6

職種: データエンジニア
雇用形態: 記載なし
給与: 記載なし
勤務地: 東京都
働き方: 記載なし
経験年数: 5年以上
掲載日: 2026/11/06 03:36（2日前）
最終確認: 2026/11/06 21:35（2日前）

仕事内容

Department Overview

You'll join a 60-person AI for Customer Service team of engineers, scientists, data analysts, and dialogue designers, responsible for modernizing legacy contact-center tooling and workloads into an agentic Generative AI stack codenamed Raptor. We already power more than 10 customer and sales support chat services. Now, we're in scale-up mode, expanding to all 70 BUs and using AI to multiply Rakuten's core capabilities in e-commerce, finance, logistics, digital content, and beyond.

Why We Hire

We are seeking a Data Engineer to build the data foundation for our next-generation customer communication as a service (CCaaS) product from the ground up. This role is responsible for the entire data backbone of a V1 product: pipelines, storage, and the data platform that our analysts and product managers will depend on from day one.
You will be a foundational member of a team dedicated to creating a revolutionary CCaaS product in a startup-like environment. If you are passionate about building first-version (V1) data infrastructure where nothing exists yet, and want your work to directly shape how a product team thinks and decides, this is the perfect opportunity for you.

Position Details

Design, build, and own the data infrastructure for the V1 CCaaS product, from ingestion through serving, supporting the business unit's analytics needs.
Develop and operate production-grade ETL/ELT pipelines with orchestration tools, ingesting and transforming the large volumes of data generated by chatbot events and customer journey interactions.
Architect and optimize cloud data warehouse datasets across medallion-style layers (raw bronze, cleansed silver, aggregated gold), with partitioning, clustering, and SQL tuning for large-scale analytical workloads.
Design event-centric data models at session and turn grain that serve both operational features and analytical reporting, keeping the product's operational layer and analytical layer consistent.
Keep every pipeline reliably dependable in production through data quality checks, monitoring, and observability, so analysts and product managers can trust the numbers they see on dashboards.
Partner with Data Analysts and Product Managers to understand data needs. Requests typically start as one-off extractions and are iterated into scalable, reusable data products as demand grows or as they feed into the main pipeline.
Integrate real-time or near-real-time data streams as the product's event volume grows.
Establish data governance fundamentals: standardized event definitions, schemas, and naming conventions across all smart agents so their data stays comparable and combinable, plus documentation, lineage, and access control appropriate for an early-stage, fast-moving team.

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