Data Engineer: Scale Etl Pipelines For Analytics

Pt Medela Potentia Tbk; Tangerang; On-site; IDR 180,000,000 - 240,000,000; Full time

Job summary

Pt Medela Potentia Tbk in Bekasi is seeking an AI Data Engineer to build and manage the data infrastructure powering their AI initiatives. You will design, construct, and maintain robust data pipelines, ensuring data availability, quality and accessibility for machine learning models and analytics.

You will collaborate closely with data scientists and ML engineers to provide the data foundation needed to succeed and enable data-driven decision-making across the organization.

Responsibilities

• Design, build, and optimize scalable and reliable data pipelines for AI and machine learning applications.
• Develop processes for data ingestion, transformation, cleaning, and storage using various data technologies.
• Implement data quality checks and monitoring to ensure the integrity and accuracy of data.
• Collaborate with data scientists and ML engineers to understand their data requirements and provide efficient access.
• Manage and maintain data warehousing solutions and big data platforms.
• Automate data

Job description

AI Data Engineer

Our client is seeking a skilled AI Data Engineer to build and manage the data infrastructure that powers their artificial intelligence initiatives in Bekasi . You will be responsible for designing, constructing, and maintaining robust data pipelines, ensuring the availability, quality, and accessibility of data for machine learning models and analytics. This role is crucial for enabling data-driven decision-making and supporting the development of advanced AI applications. You will collaborate closely with data scientists and ML engineers to provide them with the data foundation they need to succeed.

Key Responsibilities

• Design, build, and optimize scalable and reliable data pipelines for AI and machine learning applications.
• Develop processes for data ingestion, transformation, cleaning, and storage using various data technologies.
• Implement data quality checks and monitoring to ensure the integrity and accuracy of data.
• Collaborate with data scientists and ML engineers to understand their data requirements and provide efficient access.
• Manage and maintain data warehousing solutions and big data platforms.
• Automate data