Data Engineer
Company: Bukuwarung
Role summary
Data Engineer is a full-time role at Bukuwarung in South Jakarta, Java, Indonesia (on-site). Posted 2026-08-13. Experience level: 2-5.
- Job title
- Data Engineer
- Company
- Bukuwarung
- Location
- South Jakarta, Java, Indonesia
- Work arrangement
- on-site
- Employment type
- full-time
- Category
- Data Science
- Experience level
- 2-5
- Date posted
- 2026-08-13
Job description
About BukuWarung
BukuWarung is building the digital and financial infrastructure for micro and small businesses across Southeast Asia. We serve millions of MSMEs through payments (BukuPay), credit (BukuModal), and financial tools — helping underbanked entrepreneurs grow faster and more securely.
The next phase of BukuWarung's growth is data-native: real-time decisioning, smarter underwriting, fraud prevention, and deeply personalized products — built for the 100M+ MSMEs across Southeast Asia who remain underserved by traditional financial institutions. None of that ships without a unified data platform underneath it.
Why This Role Matters
BukuWarung's most valuable signals — payments transactions, device activation and usage, merchant behavior, and a growing lending book — today live in fragmented systems across channels and operations. We are building a unified data platform to consolidate all of it into a single, governed source of truth that powers credit, fraud, and company-wide analytics.
As a Data Platform Engineer , you'll be a hands-on builder on that platform. Working alongside senior engineers, you'll develop and maintain the pipelines, models, and tooling that turn raw data from every channel into clean, reliable data the whole company can use. This is a high-growth role for an engineer early in their career who wants to build real data infrastructure at scale and learn fast. You will:
Build and maintain batch and streaming pipelines that move data from payments, devices, lending, and field operations into our warehouse
Develop clean, well-tested data models and transformations that teams across the company rely on
Help keep the platform reliable and trustworthy through monitoring, testing, and data quality checks
Support analysts and ML engineers by making data accessible and easy to consume
Key Responsibilities
Pipeline & Data Development
Build and maintain data pipelines (batch and streaming) that ingest data from BukuWarung's payments, device, lending, and operations systems
Develop transformations and data models (e.g. SQL / dbt) that produce clean, well-documented datasets for analytics and ML
Contribute to streaming workflows (Kafka, Flink, or Spark Streaming) that support fraud and credit use cases, under the guidance of senior engineers
Write reliable, tested, maintainable code and participate in code and design reviews
Data Quality & Reliability
Implement data quality checks, tests, and monitoring so issues are caught before they reach downstream consumers
Help investigate and resolve pipeline failures, freshness issues, and data discrepancies
Contribute to documentation, lineage, and cataloging so datasets are discoverable and trusted
Follow governance and access practices aligned with Bank Indonesia and OJK requirements for handling sensitive financial data
Enablement & Collaboration
Partner with analysts, ML engineers, and business teams to understand data needs and deliver usable datasets and tables
Help build and maintain self-serve tooling and dashboards that let Ops, Finance, and GTM answer routine questions
Learn the modern data stack hands-on and grow toward owning larger parts of the platform over time
Requirements
Must-Have
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
Nice-to-Have
Exposure to streaming technologies (Kafka, Flink, or Spark Streaming)
Experience with a cloud platform (AWS or GCP)
Interest in or exposure to fintech, payments, or lending data
Familiarity with data quality / testing tools (e.g. Great Expectations, dbt tests)
Key Impact Areas — First 12 Months
Reliable Pipelines — Own and improve a set of production pipelines that consistently deliver fresh, accurate data
Clean Data Models — Ship well-documented, tested datasets that analytics and ML teams adopt and trust
Better Data Quality — Add tests and monitoring that measurably reduce data incidents
Growth — Grow into an engineer who can independently deliver meaningful parts of the unified data platform