Cloud Data Engineer — AI-Driven Pipelines on AWS

PT SMART,Tbk

Jakarta Selatan

On-site

IDR 279,000,000 - 502,200,000

Full time

2 days ago

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Job summary

PT SMART Tbk is seeking an experienced Cloud Data Engineer to design, build, and optimize scalable data pipelines in cloud environments. You will work with data science and analytics teams to enable BI and data-driven decisions, leveraging AI-powered tools to boost productivity and automate repetitive tasks.

The role emphasizes strong ETL/ELT development, data warehousing, SQL, and Python expertise, with cloud experience on AWS.

Qualifications

Bachelor's degree in Computer Science, Information Technology, Information Systems, or a related field.
Minimum 4 years of professional working experience as a Data Engineer with hands-on ETL/ELT data pipelines.
Strong understanding of Data Warehouse concepts, architecture, data modelling, and data integration processes.
Strong proficiency in SQL, including complex queries, query optimization, and ETL stored procedure development.
Good programming skills in Python for data processing, transformation, and automation.
Hands-on experience with at least one major cloud platform (AWS).
Familiarity with AI-powered tools to improve development efficiency.

Responsibilities

Design, develop, implement, and maintain robust ETL/ELT data pipelines to support enterprise data integration and analytics requirements.
Develop and optimize SQL queries, complex stored procedures, and database processes for extraction, transformation, and loading.
Design, build, and maintain Data Warehouse solutions, including data models and data integration workflows.
Develop and maintain data processing scripts using Python.
Implement and manage data pipelines on cloud platforms, mainly AWS and Microsoft Fabric.
Monitor, troubleshoot, and optimize data pipelines for data quality and performance.
Collaborate with cross-functional teams to translate business requirements into technical data solutions.
Apply best practices in data engineering, including data security, governance, and automation.
Leverage AI-powered development tools to accelerate coding and troubleshooting.
Continuously explore and recommend new technologies to improve data engineering capabilities.