Senior Data Engineer (GCP)

EPAM Systems; Malaysia; On-site; MYR 180,000 - 240,000; Full time

Job summary

EPAM Systems Inc is seeking a Senior Data Engineer (Google Cloud Platform) to design, build and optimize ETL/ELT pipelines and data models for scalable data warehousing on GCP/AWS. You will enhance SQL performance, orchestrate workflows with Airflow, and drive pipeline reliability across cloud data platforms.

You will collaborate with clients and delivery teams, improve CI/CD practices, and investigate issues to provide robust, production-ready data solutions.

Qualifications

• Hands-on experience building ETL and ELT pipelines and supporting data warehousing.
• Advanced SQL skill set including optimization, data validation and complex transformation.
• Python proficiency for pipeline development and software engineering practices.
• Experience with distributed processing using PySpark or Apache Beam.
• Practical cloud experience with Google Cloud Platform and AWS for data workloads.
• Workflow orchestration background with Apache Airflow or Google Cloud Composer.
• Knowledge of data modeling, data lifecycle management and data quality controls.
• Clear communication with the ability to explain trade-offs, risks and solution options to stakeholders.

Responsibilities

• Design and build scalable ETL and ELT pipelines using Python, SQL, BigQuery and cloud-native services.
• Develop and optimize complex SQL for transformation, validation, troubleshooting and performance.
• Create and maintain data models and warehousing solutions that support quality, availability and scale.
• Orchestrate workflows in Apache Airflow, including scheduling, monitoring and incident resolution.
• Support pipeline delivery and platform operations across Google Cloud Platform and AWS.
• Improve CI/CD, source control and automated deployment practices for data solution.
• Investigate data, integration and infrastructure issues, driving root-cause analysis and fixes.
• Collaborate with clients and cross-functional teams to clarify requirements, risks, dependencies and delivery plans.

Skills

ETL/ELT pipelines

SQL optimization

Python

Airflow

Data modeling

CI/CD

Root-cause analysis

Tools

BigQuery

Google Cloud Composer

AWS

PySpark

Apache Beam

Terraform

Kubernetes

Job description

We are seeking a Senior Data Engineer (Google Cloud Platform). You will build reliable ETL and ELT pipelines, optimize SQL and orchestration in Apache Airflow and help teams turn raw data into trusted, ready-to-use datasets. You will partner with clients and delivery teams to improve quality, performance and operational stability across cloud data platforms.

Responsibilities

• Design and build scalable ETL and ELT pipelines using Python, SQL,Big Query and cloud-native services
• Develop and optimize complex SQL for transformation, validation, troubleshooting and performance
• Create and maintain data models and warehousing solutions that support quality, availability and scale
• Orchestrate workflows in Apache Airflow, including scheduling, monitoring and incident resolution
• Support pipeline delivery and platform operations across Google Cloud Platform and Amazon Web Services (AWS)
• Improve CI/CD, source control and automated deployment practices for data solution
• Investigate data, integration and infrastructure issues, driving root-cause analysis and fixes
• Collaborate with clients and cross-functional teams to clarify requirements, risks, dependencies and delivery plans

Requirements

• Hands-on experience building ETL and ELT pipelines and supporting data warehousing
• Advanced SQL skill set including optimization, data validation and complex transformation
• Python proficiency for pipeline development and software engineering practices
• Experience with distributed processing using PySpark or Apache Beam
• Practical cloud experience with Google Cloud Platform and or Amazon Web Services (AWS) for data workloads
• Workflow orchestration background with Apache Airflow or Google Cloud Composer
• Knowledge of data modeling data lifecycle management and data quality controls
• Clear communication with the ability to explain trade-offs, risks and solution options to stakeholders

Nice to have

• Infrastructure-as-code exposure using Terraform and or Kubernetes
• Streaming familiarity with Kafka and event-driven patterns
• Data integration tooling experience such as Fivetran