About the work
From event data toward a clearer product decision.
Analytics Engineer and Product Analyst based in Jakarta, Indonesia. The work connects event tracking, warehouse models, metric definitions, product behavior, and business decisions.
The aim is simple: give teams data they can trust, questions they can answer, and a clear way to measure what happens next.
Bachelor of Public Health (Biostatistics), Universitas Indonesia
Real-world evidence boundary
Professional experience covers alerting, warehouse pipelines, live dashboards, campaign and survey models, and KPI work. Employer names, scale, adoption, and outcome metrics are kept private or are not verifiable in this public repository. The case studies below demonstrate independent analytical and engineering work, not employer results.
Career arc
From reporting questions to product and data systems.
BI foundations
Business Intelligence foundations turn operational questions into useful reporting.
Broader data work
Reporting expands into data pipelines, predictive models, and systems behind trustworthy analysis.
Decision systems
Reliable data connects with clear recommendations and better business decisions.
Selected experience
A progression from reporting to reliable decision systems.
A concise view of work across four analytics roles, the data foundations built, and the decisions made clearer.
Data Analyst
Oct 2024 — presentConsumer commerce platform
Event-driven alerting, warehouse pipelines, and live dashboards support customer-activity monitoring for support and product teams.
Data & Automation Consultant
Mar 2023 — Oct 2024Independent consulting
Survey and campaign data becomes reusable analytical models, automated warehouse workflows, and leadership-ready reporting.
Senior Business Intelligence Analyst
Jul 2022 — Mar 2023E-commerce platform
Churn analysis, customer segmentation, cohort analysis, and KPI governance connect retention and marketing teams to consistent customer signals.
Business Intelligence Analyst
Apr 2021 — Jun 2022E-commerce platform
Product, customer, and commercial reporting surfaces engagement patterns for roadmap decisions.
Capabilities
A practical toolkit for careful analysis.
Analytics engineering
Layered SQL models, metric definitions, event-grain thinking, data-quality checks, and reproducible pipelines.
Product analytics
Funnels, activation signals, cohorts, retention, segmentation, and product measurement plans.
Experimentation
Incrementality, holdouts, guardrails, treatment integrity, and decisions grounded in business value.
Data reliability
Source validation, freshness boundaries, reconciliation, lineage, and visible evidence limits.