GoTymeX

Builds the software behind digital banks

Lead Data Engineer - Multiple Teams

Applied Machine LearningConfirmed live in the last 24 hours

Full-timeHo Chi Minh City, VietnamHybrid

About the Job

Requirements

At least 7 years of experience in data platform architecture, cloud or machine-learning infrastructure engineering, or related roles.
Deep technical expertise in Databricks and AWS, including the ability to design, integrate, and operate solutions spanning both platforms.
Strong hands-on implementation skills, including building, deploying, and operating platforms, not only designing them.
A proven track record of building and operating scalable production machine-learning and artificial-intelligence platforms for model training and deployment.
Expertise in Apache Spark, Delta Lake, and modern batch and streaming data-pipeline frameworks.
A strong background in infrastructure as code using Terraform or CloudFormation, CI/CD for data and machine-learning workflows, and DevSecOps practices.
Proficiency in Python and SQL.
Experience with data governance, data lineage, observability, and MLOps frameworks such as MLflow, Airflow, or dbt.

Responsibilities

Architect and implement end-to-end data and machine-learning platforms, including data lakes, warehouses, streaming and batch pipelines, and model training and deployment infrastructure on AWS and Databricks.
Lead DevSecOps and DataOps practices, including infrastructure as code, CI/CD pipelines for data and machine-learning workflows, and secure multi-account and multi-region cloud operations.
Integrate AWS services such as S3, Redshift, Kinesis, Lambda, EKS, and ECS with Databricks Runtime, Delta Lake, and Unity Catalog to build scalable, performant pipelines.
Build and operate machine-learning infrastructure, including training clusters, model versioning, MLOps tooling such as MLflow, model monitoring and observability, and automatic retraining workflows.
Establish data governance, lineage, quality, and observability standards across data pipelines and machine-learning workflows.
Mentor engineering teams, define architectural best practices, and guide implementation of high-scale data and machine-learning systems.
Optimize system performance, cost, and scalability; diagnose and resolve large-scale production issues.
Continuously evaluate tools and technologies in cloud, data platforms, DevSecOps, and machine-learning infrastructure, and apply them to drive innovation.

Desired Qualifications

Experience in fintech, regulated industries, or high-security environments.
Familiarity with Scala or an equivalent language.

Lead Data Engineer (Databricks, MLops, AWS)

GoTymeX · Ho Chi Minh City, Vietnam (Hybrid) · Full-time · Posted 2026-06-11

Workplace: hybrid

Department: Data

Description

Role Overview

We are seeking a Senior/Lead Platform Engineer who will take ownership of the design, implementation and operation of our core data, analytics and ML infrastructure. This role spans across platform architecture, DevSecOps, DataOps, and ML infrastructure, and requires a combination of strategic thought leadership and hands-on execution. You will build, integrate and operate platforms on AWS and Databricks, enabling scalable, secure, production-grade ML/AI solutions.

Key Responsibilities

Architect and implement end-to-end data and ML platforms: data lakes, warehouses, streaming and batch pipelines, model training/deployment infrastructure, on AWS + Databricks.
Lead DevSecOps and DataOps practices: infrastructure as code (IaC), CI/CD pipelines for data & ML workflows, secure multi-account/multi-region cloud operations.
Integrate AWS services (e.g., S3, Redshift, Kinesis, Lambda, EKS/ECS) with Databricks runtime, Delta Lake, Unity Catalog etc to build scalable, performant pipelines.
Build and operate ML infrastructure: training clusters, model versioning, MLOps toolchain (e.g., MLflow), model monitoring and observability, automatic retraining workflows.
Establish data governance, lineage, quality, observability standards across data pipelines and ML workflows.
Mentor engineering teams, define architectural best practices and guide implementation of high-scale data/ML systems.
Optimize system performance, cost and scalability; diagnose and resolve large-scale production issues.
Continuously evaluate new tools and technologies in the areas of cloud, data platform, DevSecOps, ML infrastructure and apply them to drive innovation.

Requirements

7+ years of experience in data platform architecture, cloud/ML infrastructure engineering or related roles.
Deep technical expertise in Databricks and AWS: demonstrated ability to design, integrate and operate solutions spanning both platforms.
Strong hands-on implementation skills: you will not just design but build, deploy and operate the platform.
Proven track record of building and operating scalable ML/AI platforms in production (model training & deployment).
Expertise in Apache Spark, Delta Lake, modern data pipeline frameworks (batch + streaming).
Strong background in infrastructure as code (Terraform, CloudFormation), CI/CD for data/ML, and DevSecOps practices.
Proficiency in Python and SQL; familiarity with Scala or equivalent is a plus.
Experience with data governance, data lineage, observability and MLOps frameworks (e.g., MLflow, Airflow, dbt).
Bonus: Experience in fintech, regulated industries or high-security environments.

Benefits

Performance-based bonus and equity participation in GoTyme Group
13th month salary pro-rata
15-day annual leave+ 3-day sick leave + 1 birthday leave + 1 Christmas leave
Meal and parking allowance are covered by the company.
Full benefits and salary rank during probation.
Insurances as Vietnamese labor law and premium health care for you and your family without seniority compulsory
SMART goals and clear career opportunities (technical seminar, conference, and career talk) - we focus on your development.
Values-driven, international working environment, and agile culture.
Overseas travel opportunities for training and working related.
Internal Hackathons and company's events (team building, coffee run, blue card...)
Work-life balance 40-hr per week from Mon to Fri.

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About the Company

GoTymeX

Builds the software behind digital banks

GoTymeX builds digital banking products for consumers and small businesses through GoTyme's banks and financing operations in emerging markets. Its design, engineering and data teams create the customer applications and underlying systems used for accounts, payments, credit cards and loans. The hub supports banks in South Africa and the Philippines and financing businesses in Indonesia and Hong Kong, adapting technology to different local banking requirements. Its products combine mobile services with physical customer access through the banking group's retail partnerships. Established in 2016, GoTymeX is headquartered in Ho Chi Minh City, Vietnam.

Company size: 501-1,000
Company stage: Not listed
Total funding: Not listed
Headquarters: Ho Chi Minh City, Vietnam
Founded: 2016

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