VINFAST | Data Engineer
Hạn nộp hồ sơ: 04/09/2026 (Còn 10 ngày)
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What you'll be responsible for
1. Data acquisition & integration
1. Data acquisition & integration
- Build ingestion pipelines for: vehicle telemetry, DMS / work-order history, CRM / O2O, charging, service, navigation.
- Work directly with source owners to settle the data contract: grain, keys, cadence, and how a changed record is identified.
- Read from S3, BigQuery and Redshift in batch - hive-partitioned and safe to re-run.
2. Data platform
- Airflow 3 (MWAA) - asset-driven orchestration, no sensors.
- Spark / EMR Serverless - heavy preprocessing.
- dbt (ClickHouse adapter) - staging and mart layers.
- ClickHouse (MergeTree) and an S3 medallion lake.
- Guarantee idempotency, correct partitioning, safe re-runs and backfills, and predictable cost.
3. Data products
- Built in priority order - not all at once:
- Vehicle360 - in progress
- Mobility360 / trips - in progress
- Customer360 - blocked pending the identity spine and consent decisions
- Charging360, Service360 - backlog
- Every data product needs a clear schema, tests, documentation, and an owner.
4. Serving & data access
- For people: dashboards and self-serve views over the marts, so stakeholders stop asking us to run queries for them.
- For AI agents: a governed data-access layer - an MCP server or API over the warehouse - that lets ViTA and other agents fetch context safely, with the right filters, limits and permissions baked in rather than left to the caller.
- Make the semantics legible: an agent querying a mart should not have to guess what a column means.
5. Data quality & governance
- Freshness, volume and schema-drift checks.
- Lineage and metadata at a level that's actually useful.
- PII compliance: biometric sources supply event labels only - never raw images, voice, or embeddings. Cross-brand data requires a lawful basis before it is ever joined.
6. DevOps
- GitLab CI, Docker, AWS (IAM, S3 lifecycle, EMR).
- Logging, monitoring, and alerting when a pipeline breaks
Requirements
Must-have
Must-have
- 3+ years in data engineering, with pipelines you've actually run in production - not personal projects or coursework.
- Strong Python and SQL.
- Apache Airflow - you've written and operated your own DAGs, and you understand backfill and catchup.
- Spark / PySpark - you've handled datasets in the hundreds of gigabytes.
- Data modeling - grain, keys, SCDs, and the difference between a fact and a dimension.
- You can explain: idempotency, partition strategy, late-arriving data, and what you do when an upstream schema changes without warning.
- Practical AWS: S3, IAM, and the ability to read a permissions error and fix it.
- Communication. You'll sit with data owners outside this team. Asking the right question matters as much as writing the code.
Nice to have
- ClickHouse or another columnar OLAP engine: Druid, Doris, BigQuery, Redshift.
- Data visualization - Metabase, Superset, Grafana or similar. Enough to build a dashboard people trust, and enough taste to know when a number needs context rather than a bigger font.
- Building MCP servers or tools for AI agents - exposing warehouse data to an LLM agent as typed, permissioned tools. New for most candidates; genuine curiosity here counts as much as experience.
- EMR / Spark on serverless infrastructure, including job cost tuning.
- Kafka or CDC - not used at this stage, but on the roadmap.
- Vehicle, IoT or telemetry data experience.
- Enough English to read technical documentation.
What we are not asking for
- Stated plainly so you don't rule yourself out: you do not need Scala, Go, Iceberg, Delta Lake, MinIO, Neo4j, Qdrant, MLflow, LangGraph, Kafka
- Connect, MQTT or Debezium. None of them are in the stack today. If we need them later, we'll learn them together.
Who fits here
- Comfortable with ambiguity. You'll hit "the data didn't arrive" far more often than "this query is slow".
- Goes and asks instead of sitting and guessing.
- Treats documentation as part of the work, not an afterthought.
- Would rather leave behind a system that runs than code that's clever.
- Three things changed from the last version: VinFast is now the employer line rather than ViTAI alone; Serving & data access is a full responsibility section (dashboards for people, an MCP/API layer for agents) with a matching bullet in the first-12-months list; and the nice-to-have list gained data visualization and building MCP servers/tools. The agent-facing bullet is deliberately worded to welcome candidates who are curious but haven't shipped one - almost nobody in the VN market has yet, and screening on it as experience would empty your pipeline.
Thông tin chung
- Thu nhập: Thỏa thuận
Việc làm tương tự khác
Công Ty TNHH Giáo Dục Và Đào Tạo Vinacademy
Xem trang công ty- Địa chỉ công ty: Vinhomes Ocean Park, Đa Tốn, huyện Gia Lâm, Hà Nội
- Quy mô: Từ 101 - 500 nhân viên
- Lĩnh vực: Giáo dục / Đào tạo
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