Data Engineer — Real-Time Pipelines & Data Marts

Sea; Jakarta Pusat; On-site; IDR 200,000,000 - 500,000,000; Full time

Job summary

Monee in Indonesia is seeking an experienced Data Engineer to design and optimize batch and real-time data pipelines, with a focus on scalable feature processing.

You will build data marts, model data, and ensure high data quality while collaborating with data science, policy, risk, and business teams to meet evolving data requirements.

This role offers opportunities to work on core infrastructure and contribute to data platforms across markets.

Qualifications

• 3+ years of experience in data engineering or big data development, with strong SQL and Python/Scala/Java skills.
• Hands-on experience with Spark/Flink for batch or stream processing.
• Solid understanding of data warehousing, dimensional modeling, and Data Mart development.
• Familiarity with big data technologies such as Kafka, Hive, and Airflow.
• Preferred Qualifications: Experience with Feature Engineering, Feature Stores, or data/feature platforms.

Responsibilities

• Develop, optimize, and maintain batch and real-time feature pipelines using Spark/Flink.
• Design, build, and maintain business Data Marts, including data modeling, ETL development, and performance optimization.
• Ensure data quality, accuracy, and pipeline reliability, continuously improving data processing efficiency.
• Collaborate with DS, policy, risk and business teams to support feature engineering and data requirements.

Skills

SQL

Python

Scala

Java

Tools

Spark

Flink

Kafka

Hive

Airflow

Job description

Monee in Indonesia is seeking an experienced Data Engineer to design and optimize batch and real-time data pipelines, with a focus on scalable feature processing.

You will build data marts, model data, and ensure high data quality while collaborating with data science, policy, risk, and business teams to meet evolving data requirements.

This role offers opportunities to work on core infrastructure and contribute to data platforms across markets.