Data Engineer

VinFast is a pioneering electric vehicle (EV) company committed to revolutionizing the automotive industry with sustainable and innovative mobility solutions. As a leading player in the EV market, VinFast is dedicated to delivering high-quality, cutting-edge electric vehicles that redefine the driving experience. Our team consists of passionate professionals driven by a shared vision of creating a greener and more sustainable future through innovation, technology, and excellence. The mission Build the data foundation for VinFast's AI Mobility Assistant (ViTA) and the AI agents running across the VinFast ecosystem. To be direct about where we are: this platform is in its foundation phase, not its optimisation phase. One reference lane - vehicle telemetry - runs end to end from S3 through ClickHouse into dbt. The other ten registered sources have no recurring feed at all. Most of your job is turning ad-hoc data drops into scheduled, dependable feeds, not tuning dashboards. If you want a stable system to operate, this isn't it. If you want to be the person who builds it, this is exactly it. What you'll be responsible for 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 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.

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 With respect to all your personal data shared to VinFast in the application and the entire recruitment process of VinFast, by clicking “Apply”, submitting your resumé/CV and/or participating in VinFast's recruitment process, you agree that you have read VinFast's Personal Data Protection Policy ("Policy") posted at https://vinfastauto.com/vn_vi/dieu-khoan-phap-ly or https://vinfast.vn/privacy-policy/, you agree to the Policy and consent for VinFast to process your personal data in accordance with the Policy and the applicable regulations on personal data protection. To all recruitment agencies: VinFast does not accept agency resumes. Please do not forward resumes to our careers alias or other VinFast employees. VinFast is not responsible for any fees related to unsolicited resumes.