Principal Data Engineer

Role summary

Principal Data Engineer is a full-time role at Bukuwarung in Hyderabad, Telangana, India (on-site). Posted 2026-08-13. Experience level: 5-10.

Job title

Principal Data Engineer

Company

Bukuwarung

Location

Hyderabad, Telangana, India

Work arrangement

on-site

Employment type

full-time

Category

Data Science

Experience level

5-10

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

Today, BukuWarung's most valuable signals — payments transactions, device activation and usage, merchant behavior, and a growing lending book — live in fragmented systems across distribution channels, hardware operations, product, and finance. Fraud, credit, and growth teams each stitch together their own view of the truth. That slows decisions and caps how good our models can be.

As Principal Data Platform Engineer , you are the hands-on technical owner of the unified data platform that fixes this. You will architect and build the streaming and batch backbone that consolidates every channel and operation into a single, governed, trustworthy source of truth — the foundation that credit underwriting, real-time fraud detection, and company-wide analytics all depend on. You will:

Architect and build a unified data platform that consolidates all distribution channels and operations into a single internal system

Stand up streaming + batch pipelines that deliver low-latency data for fraud, credit, and real-time merchant decisioning

Make clean, governed, self-serve data a company-wide resource that every business unit can trust

Set the technical standard for data engineering as the team scales — through architecture, patterns, and mentorship rather than headcount alone

Key Responsibilities

Unified Data Platform & Architecture

Own the end-to-end architecture of BukuWarung's unified data platform — ingestion, storage, transformation, serving, and access — as a single source of truth across payments, credit, fraud, hardware ops, and GTM

Consolidate fragmented, channel-specific data sources (EDC/POS, QRIS soundboxes, digital payment flows, lending, field sales, logistics) into a coherent, well-modeled warehouse and lakehouse

Design the semantic and data-modeling layer so metrics are defined once and consumed consistently across every business unit

Make deliberate build-vs-buy decisions across the modern data stack (warehouse/lakehouse, orchestration, transformation, catalog, BI) balancing cost, control, and speed

Streaming & Real-Time Data Pipelines

Build near-real-time streaming pipelines (Kafka, Flink, or Spark Streaming) that power low-latency fraud detection and credit decisioning

Design and operate reliable batch pipelines and orchestration (e.g. Airflow/Dagster) for reporting, model training, and reconciliation workloads

Deliver a feature store and serving layer so ML teams can move from a model in a notebook to a production decision with consistent online/offline features

Engineer for scale and cost efficiency as transaction volume grows, with clear SLAs on latency, freshness, and throughput

Data Quality, Governance & Reliability

Build data quality, testing, and observability into every pipeline — particularly for credit and fraud data, where errors carry direct financial consequences

Establish lineage, cataloging, and documentation so data is discoverable, trustworthy, and auditable end to end

Implement governance, access controls, and PII handling suited to Bank Indonesia regulations, OJK compliance requirements, and cross-border fintech data standards

Define and enforce SLAs for data freshness, completeness, and reliability, with alerting and clear ownership when things break

Enablement & Self-Serve

Build reusable frameworks, tooling, and paved paths that let Ops, Finance, and GTM self-serve on routine data questions without engineering bottlenecks

Deliver a governed BI and metrics layer that drives action — dashboards teams actually decide from, not reports that go unread

Partner with analysts, ML engineers, and risk scientists to make the platform genuinely productive for the people who build on it

Evaluate and integrate no-code / low-code tooling where it accelerates business teams safely

Technical Leadership

Set data engineering standards — architecture reviews, coding patterns, CI/CD for data, and infrastructure-as-code — that the team can scale on

Mentor engineers and analysts, raising the technical bar as the data organization grows from its current core team

Be a credible technical partner to product, engineering, and business leaders, translating platform decisions into their impact on capital allocation, risk, and speed

Contribute hands-on to the hardest parts of the system — this is a builder role, not a purely supervisory one

Requirements

Must-Have

8+ years in data engineering, with a track record of architecting and building data platforms at scale — ideally including fintech (payments, lending, or fraud/risk)

Deep, hands-on expertise designing unified data platforms: warehouse/lakehouse, ingestion, transformation (e.g. dbt/Spark), and orchestration (e.g. Airflow/Dagster)

Strong experience with streaming architectures (Kafka, Flink, or Spark Streaming) for real-time, low-latency decisioning

Expert in SQL and Python; comfortable with distributed data processing and cloud data infrastructure (AWS/GCP)

Proven ownership of data quality, governance, lineage, and reliability in a production, high-stakes environment

Experience enabling ML and analytics teams — feature stores, serving layers, and self-serve tooling

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