Senior Data Engineer (Data Platform)

Senior Data Engineer（Data Platform）

About Mico Mico's mission is to empower every brand by building lifetime trust through humanlike technology. By 2030, we aim to be Asia's No.1 Brand Empowerment Company. Mico builds the conversational layer Japanese brands use to reach their customers - LINE, SMS/RCS, Voice AI and web - for more than 5,500 companies across finance, insurance, retail and real estate. Our four core values guide everything we do: Wow the Customer Invest in Passion Beyond Borders Be the Change

About the Role The Data Platform team was newly established in May 2026. Our mission is not a one-off cleanup of the data models in mmc-mono-repo - the codebase behind Mico Engage AI - but building a capability to govern them continuously. As a Senior Data Engineer, you will own - from investigation through implementation and production rollout - the analysis of entity design and access patterns across TiDB and Aurora, the build-out of our governance toolset (seven items: local reproduction environments, external-expert collaboration, data lineage, test reliability, benchmarks, the classification table, and ADRs), and the concrete work that takes load off TiDB: production deletion under a retention policy, event-attribute transformation, and offloading heavy aggregation to Snowflake with reverse-ETL back into Aurora.

Beyond that lies a succession of core entities such as customer and segment. This is not a greenfield analytics platform role. It is the work of safely reshaping the data model of a large system that is already running, on the basis of evidence.

What We Expect

Take full ownership of the entities and tooling items in your scope - from investigation through architecture, implementation, production rollout, and steady-state operations.

Base optimization decisions on measurement and lineage rather than experience and intuition, and write down the reasoning.

Align directly with the business side (CE / CS) and product, and land changes with real business impact - such as deletions and segment-definition migrations - safely.

Validate governance tooling on real cases, compounding it so that the next project is faster and safer.

Build out lineage diagrams, runbooks, and ADRs so that new members can get productive on their own.

Note how success is measured here: not "how many features shipped" but "how much the toolset's minimum-viable level rose." Phase-one output may look small - we are looking for someone willing to take that on with us.

Requirements

Education

Bachelor's degree or above in computer science, information engineering, or another related quantitative or information field (equivalent practical experience is accepted in place of a degree)

Experience

5+ years of professional experience in backend or data engineering, with expertise in the following:

Must have: backend development experience(at least 3~5 years)

Nice to have: TypeScript / Node.js: Ability to write production-grade code. Experience implementing and refactoring within a large monorepo and shared libraries.

SQL and relational data modeling: Advanced SQL, schema design, judgment on normalization versus denormalization, and index design. Able to articulate entity relationships and their business meaning.

Operating and tuning RDBMS: In MySQL-compatible environments (Aurora MySQL, TiDB, etc.) - reading execution plans, fixing slow queries, assessing the impact of new indexes, and running DDL safely against large tables.

Reading existing systems: Able to trace data access paths and dependencies through a large, under-documented codebase and turn them into diagrams and documentation.

Testing and CI: Experience identifying and fixing the root causes of flaky tests (parallel execution conflicts, state leakage such as singleton leakage, insufficient isolation of external dependencies such as Redis). Working experience with CI (GitHub Actions, etc.) and containers (Docker).

Measurement-driven optimization: Experience designing your own benchmarks or profiles and demonstrating improvement quantitatively.

Cloud: Professional AWS experience (primarily Aurora / RDS, S3, IAM, networking).

Operations and reliability: Professional experience with monitoring and alert design, on-call, incident response, and root cause analysis.

Preferred Qualifications

Distributed SQL databases: Experience operating and tuning distributed SQL databases such as TiDB (practical understanding of GC, region splitting, chunk sizing, and similar).

Data warehouse: Experience building aggregation pipelines on Snowflake, and designing or operating reverse-ETL (writing results from the warehouse back into an operational database).

Retention and privacy: Experience designing and executing production data deletion under a retention policy. PII classification and data design informed by Japan's Act on the Protection of Personal Information (APPI) or the GDPR.

Automating lineage: Experience extracting data lineage automatically (e.g. via static code analysis) and generating documentation with mermaid / d2.

Infrastructure as Code: Experience managing infrastructure as code with Terraform, AWS CDK / CloudFormation, etc.

Legacy modernization projects: Experience splitting a monolith, dismantling a shared library, or completing a phased migration without service interruption.

Documentation practice: Day-to-day use of ADRs, design docs, and runbooks.

Vendor collaboration: Experience collaborating with database or warehouse vendor specialists on decisions with real technical consequence.

Community involvement: Contributions to OSS, technical writing, and speaking at meetups or conferences.