Data Engineer

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