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Caddi

Manufacturing AI platform integrating engineering data

Data Engineer - Analysis Platform

Data EngineeringConfirmed live in the last 24 hours
Full-timeHo Chi Minh City, Vietnam

About the Job

Requirements

  • At least 5 years of professional experience as a Data Engineer, or as a software engineer whose work was primarily building data pipelines and data platforms.
  • Strong SQL skills, including writing, debugging, and optimizing complex analytical queries and modeling data for analytical workloads.
  • Proficiency in Python for data processing, pipeline development, and automation.
  • Hands-on experience designing and operating production ETL/ELT pipelines, including orchestration, scheduling, backfills, and failure handling, using tools such as Airflow, dbt, Argo Workflows, Dagster, or Spark, or equivalent tools.
  • Experience with a cloud data warehouse or large-scale data processing platform such as BigQuery, Redshift, Snowflake, Databricks, Spark, or Hadoop, including understanding cost and performance trade-offs.
  • Experience developing with public cloud platforms such as Google Cloud or AWS.
  • Fluent business communication in English sufficient for daily work, including written communication and meetings; CEFR B1 or higher.
  • Must currently reside in Vietnam or plan to relocate. Foreign nationals must hold a valid Vietnam work permit or be legally eligible to work in Vietnam.

Responsibilities

  • Build and operate batch and streaming pipelines that bring data from databases, event streams, and SaaS tools into BigQuery.
  • Model the warehouse so business concepts are defined once and reused throughout the organization.
  • Keep pipelines and queries fast, reliable, and cost-efficient.
  • Set up data contracts, automated testing, and freshness monitoring to catch breakages before stakeholders find them.
  • Establish ownership, documentation, and access control for sensitive customer data.
  • Improve data and annotation quality to directly raise machine learning model performance in collaboration with the machine learning team.
  • Work with product managers and analysts to turn business questions into datasets they can self-serve.
  • Own core metric definitions and event taxonomy so everyone reports the same numbers.
  • Shorten the time between forming a hypothesis and having the data to validate it.

Desired Qualifications

  • Experience with dbt for warehouse modeling, testing, and documentation.
  • Experience with streaming or event-driven data processing using Cloud Pub/Sub, Kafka, Apache Beam/Dataflow, or Spark Structured Streaming.
  • Experience building and operating data lakes, lakehouses, or feature stores.
  • Experience implementing initiatives to improve data quality for data-centric machine learning model improvement.
  • Experience with data quality or observability tooling and practices, including data contracts, testing frameworks, lineage, and anomaly detection.
  • Experience planning and driving data utilization initiatives internally or externally using tools such as BigQuery, Redash, Looker, or Metabase.
  • Hands-on experience with a statically typed language such as Scala, Java/Kotlin, Go, TypeScript, or Rust.
  • Experience with infrastructure as code and CI/CD using Terraform, GitHub Actions, or Kubernetes.
  • Experience developing machine learning pipelines using Vertex AI Pipelines, Kubeflow, Apache Beam, or Spark.
  • Experience collaborating with machine learning engineers to continuously improve and deliver machine learning and data science models.
  • Familiarity with at least one machine learning or artificial intelligence framework such as scikit-learn, PyTorch, or TensorFlow.
  • Basic knowledge of statistics, linear algebra, and core computer science concepts behind artificial intelligence, including vector spaces, embeddings, and inference.
  • Experience leading design, development, and operations as a team lead or project driver, regardless of project size.
  • Experience working with Scrum or Agile methodologies.
  • Conversational-level Japanese proficiency; JLPT N2 or above is a guideline.

About the Company

Caddi

Manufacturing AI platform integrating engineering data

CADDi builds a Manufacturing AI Data Platform that structures fragmented data from engineering, procurement, production, and quality so humans and AI can reason across drawings and supply-chain records. Its products include CADDi Drawer/Explorer for drawing intelligence, CADDi Quote, CADDi Agent, and domain workflow tools used by Hitachi, Subaru, and Kawasaki in 20+ countries. The platform unifies diverse data sources into a shared model to enable automated workflows across design, sourcing, production, and quality. Its goal is to expand adoption in North America and grow its AI capabilities, supported by funding and strategic partnerships.

Company size
501-1,000
Company stage
Series D
Total funding
$415.6M
Headquarters
Chicago, IL
Founded
2017

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