• 
  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.

• 
  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.

• 
  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.

• 
  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.

• 
  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.

• 
  GitLab CI, Docker, AWS (IAM, S3 lifecycle, EMR).
• 
  Logging, monitoring, and alerting when a pipeline breaks
  ​
  ​

Requirements

• 
  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.

• 
  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.

• 
  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.

• 
  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.

Benefits

• 
  Competitive salary
• 
  Premium healthcare package, including PVI insurance & annual health check-ups
• 
  13th-month salary & performance bonuses to reward your contributions
• 
  Enjoy preferential pricing for services within the Vingroup ecosystem including Vinmec, Vinpearl, and Vinschool...
• 
  Opportunity to collaborate with and learn from industry-leading professionals in the automotive domain

Work Location:
Technopark Tower, Gia Lam, Hanoi, Viet Nam

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