Senior Data Analyst in Kota Jakarta Selatan at bukuwarung

About BukuWarung

BukuWarung builds the financial infrastructure for micro and small businesses in Southeast Asia. We serve merchants through payments (BukuAgen and BukuPay), credit (BukuModal), savings and banking (BukuSimpan), and a growing hardware fleet of EDC terminals and Bukupe QRIS soundboxes. Four business lines, one merchant.

The next phase is data native: real time decisioning, thin file underwriting, fraud prevention, and a distribution engine that runs on evidence rather than instinct.

Why this role matters

We are scaling from roughly 25k devices deployed per month to 55-60k by December, from 164k to 420k active devices under support, from 8M to 80M transactions monitored monthly, and a lending book growing about 3x against a tighter loss bar. Every one of those curves is a decision problem. This role owns the analytics that answer those problems—metrics, dashboards, and models that inform GTM decisions, capital allocation, and operational priorities.

What you will own

Metrics and analysis across business lines

• Build and maintain metrics across Agent/EDC, Retail/BukuPay, Lending/BukuModal, and BaaS/BukuSimpan. Own the definitions and certify them across Finance, GTM, and leadership reports

• Partner with Product, Engineering, and Ops to understand data needs and surface insights that drive decisions

• Track portfolio vintage curves, roll rates, delinquency trends, and leading indicators for the lending business

Business partnering and reporting

• Embed with business teams to understand their questions and turn them into analytics and dashboards

• Own the weekly GTM review packs per business line and monthly Business-Finance-GTM analyses

• Turn ad-hoc questions into standing reports and self-serve dashboards

• Communicate findings to leadership with clarity and business context

GTM and economics analytics

• Channel economics: CAC, payback period, contribution margin, and retention by cohort across OTS, digital, and partnerships

• Incentive scheme analysis: measure whether programs are buying the intended behaviour and detect gaming

• Merchant cohort and LTV analysis to inform acquisition targeting and budget allocation

Device and operations analytics

• Activation quality, transaction velocity post-activation, and dormancy indicators for the EDC and Bukupe fleets

• Contribution per device analysis, including refurbishment and 3PL costs

• Partner fulfillment visibility: dispatch timelines, SLA adherence, device mapping accuracy

Experimentation and analysis

• Support test design and analyse results for product and GTM initiatives

• Build quasi-experimental analyses where randomisation is not possible (geo-splits, staggered rollouts)

Data quality and self-serve

• Flag data quality issues and work with Engineering and Product to resolve them

• Build the semantic layer that enables business teams to answer their own routine questions through dashboards and reporting tools

• Document data logic and maintain documentation of key metrics and definitions

Requirements

Must have

• 5–7 years in analytics, with hands-on experience building metrics, dashboards, and analyses

• Fintech, payments, marketplace or on-demand at scale. Comfortable navigating transaction-level data

• Fluent in SQL and Python. Hands on with a modern BI and semantic stack (dbt, Looker, Metabase, or Superset)

• Strong grasp of unit economics: MDR and take rate, CAC payback, LTV to CAC, contribution margin per device or merchant

• Track record of replacing manual reporting with automated dashboards and retiring shadow spreadsheets

• Ability to communicate clearly with non-technical stakeholders. Can turn an analysis into a business insight

Nice to have

• Indonesia or Southeast Asia, with exposure to Bank Indonesia and OJK reporting requirements

• Lending or credit analytics experience: portfolio analysis, vintage analysis, early warning systems

• Field force or agent distribution analytics with 100+ frontline staff

• Device telemetry or IoT fleet analytics

• Experimentation design and quasi-experimental methods

First 12 months

• Metrics certified and live across the four business lines, reconciled to Finance

• Weekly GTM and monthly Business-Finance-GTM packs automated and manual assembly retired

• Channel and incentive economics live and visibly informing next quarter budget allocation

• Device economics dashboard live: activation quality, dormancy, and contribution per device, each tied to an intervention owner

• Lending portfolio reporting live weekly: portfolio health, roll rates, and leading indicators

• Standing dashboards and self-serve reporting reducing ad-hoc analysis volume