Data Engineer at GTEMAS

Confirmed open

This GTEMAS data-engineering role supports analytics and ML teams with reliable pipelines, documented datasets and cloud warehouse design. It covers middle and senior levels.

GTEMAS

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Location: Remote
Advertised region: VN / Vietnam
Employment: CONTRACTOR
Work arrangement: remote
Technologies: Python, AWS, Azure, SQL

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Role overview

You build and own the data infrastructure that analytics and ML teams depend on. At the Middle level, you build and extend ETL/ELT pipelines within an existing warehouse architecture. At the Senior level, you also design the warehouse's data model from scratch, set data-quality and governance standards, and are the person other engineers escalate a broken pipeline to at 2am.

Responsibilities

Build ETL or ELT pipelines with Python, Spark and Airflow; model data and optimize Snowflake, BigQuery or Redshift warehouses for performance and cost.
Set data-quality and governance checks, work with scientists and analysts on usable datasets, and at senior level own warehouse architecture and standards.
Apply testing and CI/CD practices to pipelines and use AI-assisted tools for code and anomaly detection where useful.

Qualifications

At least three years of data engineering for middle level or five for senior level, with advanced Python and SQL.
Experience with Spark or Kafka, Airflow or Prefect and a cloud data platform such as AWS Glue, EMR or Azure Data Factory.
Fluent written and spoken English, understanding of pipeline testing and data contracts; senior applicants need architecture and governance leadership.

Technology in this role

Python: Build ETL or ELT pipelines with Python, Spark and .
AWS: Experience with Spark or Kafka, Airflow or Prefect and a cloud data platform such as AWS Glue, EMR or Azure Data .
Azure: Experience with Spark or Kafka, Airflow or Prefect and a cloud data platform such as AWS Glue, EMR or Azure Data .
SQL: At least three years of data engineering for middle level or five for senior level, with advanced Python and .
Kafka: Experience with Spark or Kafka, Airflow or Prefect and a cloud data platform such as AWS Glue, EMR or Azure Data .
Spark: Build ETL or ELT pipelines with Python, Spark and .
Airflow: Build ETL or ELT pipelines with Python, Spark and .
Snowflake: model data and optimize Snowflake, BigQuery or Redshift warehouses for performance and .
CI/CD: Apply testing and CI/CD practices to pipelines and use AI-assisted tools for code and anomaly detection where .
BigQuery: model data and optimize Snowflake, BigQuery or Redshift warehouses for performance and .
Redshift: model data and optimize Snowflake, BigQuery or Redshift warehouses for performance and .

Role-specific candidate guide

Preparation notes based on the employer’s stated responsibilities and qualifications.

Understanding the scope. This Data Engineer position at GTEMAS is best assessed through the work described in the employer’s posting, rather than the title alone. The available role overview says: “You build and own the data infrastructure that analytics and ML teams depend on. At the Middle level, you build and extend ETL/ELT pipelines within an existing warehouse architecture. At the Senior level, you also…” Read that statement together with the responsibilities below to understand the likely balance between delivery, coordination and technical decision-making. The role belongs to the technical area, but that label is only a guide to the work; the employer’s own requirements remain decisive. Before applying, check the original listing for reporting lines, product or team assignment, employment terms and any details that may have changed since this copy was checked. Where this page says a fact is not specified, treat it as an open question rather than an implied promise.

What the responsibilities signal. The posting specifically names “Build ETL or ELT pipelines with Python, Spark and Airflow; model data and optimize Snowflake, BigQuery or Redshift warehouses for performance and cost.”; “Set data-quality and governance checks, work with scientists and analysts on usable datasets, and at senior level own warehouse architecture and standards.”; “Apply testing and CI/CD practices to pipelines and use AI-assisted tools for code and anomaly detection where useful.”. For each duty, identify a project where you performed comparable work and be ready to describe the context, your own contribution, constraints, decisions, and measurable result. Explain how you moved from the initial need to implementation, review, release or operational follow-through, where those stages apply. If the work involved other teams or users, distinguish your decisions from the group’s contribution. Avoid presenting a technology keyword as proof by itself: connect it to the problem you solved, the scale or complexity involved, and what changed after your work. The source may contain additional duties; review it directly before preparing your application.

How to read the requirements. The listed qualifications include “At least three years of data engineering for middle level.

About GTEMAS

GTEMAS (Global Tech Masters) presents itself as a global engineering partner and talent ecosystem. It connects businesses with software professionals to build digital products, scale delivery teams and support longer-term engineering partnerships. The company describes a model that combines technical assessment, structured delivery and professional development for its engineering community.

Its public materials highlight work across software engineering, quality, cloud and security, with an emphasis on measurable delivery, learning and client collaboration. A role at GTEMAS may serve a client engagement, so the specific product, customer context and day-to-day team can differ by assignment. Candidates should ask which project this vacancy supports and how responsibilities are divided between the GTEMAS team and the client.

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