VN Technology Data Engineer - Analysis Platform

About this role

RECRUITMENT BACKGROUND & EXPECTED ROLE
CADDi is on a mission to "Unleash the potential of manufacturing."
We operate "CADDi DRAWER," a cloud-based system that supports digital transformation centered on the use of drawings, which are the most essential data in the manufacturing industry.
One of the issues we are currently facing in the development of this rapidly growing product is developer productivity. The inefficiencies in the development environment have become more noticeable, and they are becoming a hindrance to the development of features and a degradation of the developer experience. As an activity to resolve these issues, the need for Platform Engineering is increasing.
There are various interpretations and approaches to Platform Engineering in the world, but here, we will be working on providing tools and infrastructure to increase the development velocity by “separating concerns.”

EXAMPLES OF ANTICIPATED TASKS
You will primarily work on the following (not limited to):
Data pipelines & warehouse

• Build and operate batch and streaming pipelines that bring data from our databases, event streams, and SaaS tools into BigQuery.

• Model the warehouse so business concepts are defined once and reused everywhere.

• Keep pipelines and queries fast, reliable, and cost-efficient.
Data quality & governance

• Set up data contracts, automated testing, and freshness monitoring so breakages are caught before stakeholders find them.

• Establish ownership, documentation, and access control for sensitive customer data.

• Improve data and annotation quality to directly raise ML model performance, together with the ML team.
Analytics enablement

• Work with PMs and analysts to turn business questions into datasets they can self-serve.

• Own the core metric definitions and event taxonomy so everyone reports the same number.

• Shorten the time between "we have a hypothesis" and "we have the data to validate it."
* Besides the team we are recruiting for this time, you may be assigned to other teams depending on your experience and preferences. (In that case, we would be happy to discuss this with you at the interview.)
* After joining the company, your role may change due to organizational growth or an individual’s career perspective.

INTEREST AND EXPERIENCE GAINED FROM THIS POSITION

• Ownership of a data platform from the ground up, rather than maintaining someone else’s — your design decisions will shape how the company reads its own product for years.

• Close collaboration with ML engineers and product management members, allowing you to expand your responsibilities depending on your interests and initiative.

• The fun of integrating a complex domain into a system.

• Experience in solving difficult problems with highly motivated team members.

• Experience in contributing to the scale of a product with technical skills.

• Experience in developing products that are deployed globally.

• Experience in providing value to society through the development of products that change an industry.

ORGANIZATION
Currently, the Analysis Platform team focuses on supporting modeling, ML serving, and operational workflows. Looking ahead, we aim not only to reduce the cognitive and operational burden around machine learning, but also to build a robust foundation for ML verification — ultimately shortening the lead time needed to validate business value.
To achieve this, the Analysis Platform team will expand its scope beyond traditional MLOps into data engineering proper: reliable pipelines, a well-governed warehouse, improved annotation efficiency, and mechanisms that enable faster and more reliable value validation. This role sits at the center of that expansion, working alongside ML engineers, platform engineers, and product teams.
Requirements
MUST-HAVE REQUIREMENTS

• 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 — able to write, debug, and optimize complex analytical queries, and to model data for analytical workloads (not just query existing tables).

• Proficiency in Python for data processing, pipeline development, and automation.

• Hands-on experience designing and operating ETL/ELT pipelines in production, including orchestration, scheduling, backfills, and failure handling (e.g. Airflow, dbt, Argo Workflows, Dagster, Spark, or equivalent).

• Experience with a cloud data warehouse or large-scale data processing platform (BigQuery, Redshift, Snowflake, Databricks, Spark/Hadoop, or equivalent), including an understanding of cost and performance trade-offs.

• Experience in development using public cloud platforms such as Google Cloud, AWS, etc.

• A sense of ownership over data correctness — you treat a broken dashboard or a silently wrong number as your problem, and you build the checks that prevent it next time.

• Fluent business communication skills in English, able to complete daily tasks in English, including text communication and meetings (CEFR B1 or higher).

• Must currently reside in Vietnam or have plans to relocate. Foreign nationals must also hold a valid Vietnam work permit or be legally eligible to work in Vietnam.

NICE-TO-HAVE REQUIREMENTS

• Experience with dbt for warehouse modeling, testing, and documentation.

• Experience with streaming or event-driven data processing (Cloud Pub/Sub, Kafka, Apache Beam / Dataflow, Spark Structured Streaming).

• Experience building and operating Data Lakes, Lakehouses, or Feature Stores.

• Experience implementing initiatives to improve data quality for data-centric ML model improvement.

• Experience with data quality / observability tooling and practices — data contracts, testing frameworks, lineage, 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…