Degree in Informatics Engineering

Indonesia

Cumulative GPA: 3.58/4.00
Relevant Coursework: Multivariate Data Analysis, Software Engineering, Operating Systems, Algorithms, Artificial Intelligence, Probability and Statistics, Database Management, Computational Intelligence, and Data Mining.

Data Scientist

• Built and deployed ML models for customer segmentation, demand forecasting, and risk scoring using Scikit-learn, TensorFlow, and PyTorch.
• Designed a scalable anomaly detection for 9M+ daily e-commerce records, detecting abnormal sales spikes, scraping errors, and metadata inconsistencies.
• Implemented self-cleaning logic and data quality rules (e.g., for price/title jumps, missing values) to ensure clean, reliable datasets for downstream analysis.
• Developed hybrid rule-based and ML-based logic to validate sales spikes, tag campaign-driven behavior, and detect anomalies in e-commerce trends.
• Automated P&L reporting and financial ETL processes, improving SLA adherence and saving 3+ hours of manual work daily.
• Delivered real-time insights and visualizations using Python (Pandas, Seaborn) and enhanced stakeholder dashboards.
• Built NLP solutions for chatbots, text classification, and smart document search to support automation and efficiency.

Data Analyst

• Designed and maintained data marts and analytical datasets in PostgreSQL and Oracle to support internal reporting, dashboards, and business decision-making.
• Created and optimized complex SQL queries to extract insights, update legacy logic, and support evolving business requirements.
• Automated manual data processes using Python scripts, including scheduled email reports and database migration tasks, reducing recurring workload for operations teams.
• Collaborated with software engineers to build and integrate MFIN, an internal dashboard system, by delivering robust and accurate SQL back-end logic.
• Automated database migration with Python scripts, reducing migration time by 90% (from 1 hour to 5 minutes).
• Designed and implemented interactive Power BI dashboards, eliminating 10 hours of manual reporting per week.
• Developed stored procedures and materialized views to support ad hoc data analysis requests from product and finance teams, improving report turnaround time.
• Partnered with cross-functional stakeholders to define business rules and translate reporting requirements into reusable SQL and data models.

Data Engineering Analyst

• Built and maintained scalable Airflow DAG templates supporting 5,000+ dynamic data quality checks across key domains (e.g., revenue, churn), covering six dimensions: completeness, timeliness, consistency, accuracy, uniqueness, and validity.
• Led and supported an L1 team (3–6 members), optimizing data quality dimensions (completeness, timeliness, consistency, accuracy, uniqueness, and validity), reducing project timelines by 2 months.
• Collaborated with client’s DQM lead and data governance teams to implement validated configurations, ensuring KPIs exceeded 90%+.
• Designed and optimized SQL queries (Oracle, Hive), with final outputs validated in PostgreSQL via Kafka streams.
• Managed and troubleshot distributed infrastructure, including Hadoop memory cleanup, Kubernetes pod restarts, and DAG performance tuning.
• Developed SOPs and automation tools to accelerate L1 task execution and configuration rollout, completing the implementation phase 1 months ahead of schedule. 
• Maintained platform stability by addressing incidents via Jira and collaborating with developers on system-level issues.