Data Platform Engineer: Real-Time Data at Scale

BukuWarung; Jakarta Pusat; On-site; IDR 180,000,000 - 240,000,000; Full time

Job summary

BukuWarung is building a unified data platform to consolidate payments, devices, lending, and operations data into a single source of truth. As a Data Platform Engineer, you will develop pipelines and models, ensuring data is reliable and accessible for analytics and ML across teams.

You will work closely with analysts, ML engineers, and business teams, learning modern data stack technologies and growing toward owning larger platform components while contributing to governance and data quality

Qualifications

• 2–4 years of experience in data engineering, software engineering, or a closely related data role
• Strong SQL and solid Python; comfortable writing clean, testable code
• Hands-on experience building or maintaining data pipelines and working with a data warehouse (e.g. BigQuery, Snowflake, Redshift)
• Familiarity with a workflow orchestrator (e.g. Airflow, Dagster) and transformation tooling (e.g. dbt or Spark)
• Understanding of data modeling fundamentals and why data quality matters
• Eagerness to learn, take feedback, and grow quickly in a fast-moving environment

Responsibilities

• Build and maintain batch and streaming data pipelines from multiple source systems into the data warehouse
• Develop clean, well-documented data models and transformations for analytics and ML
• Ensure platform reliability through monitoring, tests, and data quality checks
• Enable analysts and ML engineers by making data accessible and easy to consume

Skills

SQL

Python

Data pipelines

Data warehouse

Airflow

dbt

Spark

Data modeling

Tools

BigQuery

Snowflake

Redshift

Job description

BukuWarung is building a unified data platform to consolidate payments, devices, lending, and operations data into a single source of truth. As a Data Platform Engineer, you will develop pipelines and models, ensuring data is reliable and accessible for analytics and ML across teams.

You will work closely with analysts, ML engineers, and business teams, learning modern data stack technologies and growing toward owning larger platform components while contributing to governance and data quality