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I am a results-driven Data Engineer with a 3.94 GPA and hands-on experience building and optimizing scalable ETL pipelines in AWS and hybrid environments. I excel at implementing data quality frameworks and consolidating pipelines, with expertise in Python, SQL, AWS services, and Apache Hop, and a track record of delivering automated dashboards and cost efficiencies across fintech and tech teams.…I am a results-driven Data Engineer with a 3.94 GPA and hands-on experience building and optimizing scalable ETL pipelines in AWS and hybrid environments. I excel at implementing data quality frameworks and consolidating pipelines, with expertise in Python, SQL, AWS services, and Apache Hop, and a track record of delivering automated dashboards and cost efficiencies across fintech and tech teams.

Ammar Pavel Zamora Siregar

Data Scientist, Data Analyst, Full Stack Developer, +2

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I am a results-driven Data Engineer with a 3.94 GPA and hands-on experience building and optimizing scalable ETL pipelines in AWS and hybrid environments. I excel at implementing data quality frameworks and consolidating pipelines, with expertise in Python, SQL, AWS services, and Apache Hop, and a track record of delivering automated dashboards and cost efficiencies across fintech and tech teams.…I am a results-driven Data Engineer with a 3.94 GPA and hands-on experience building and optimizing scalable ETL pipelines in AWS and hybrid environments. I excel at implementing data quality frameworks and consolidating pipelines, with expertise in Python, SQL, AWS services, and Apache Hop, and a track record of delivering automated dashboards and cost efficiencies across fintech and tech teams.

Available to hire

I am a results-driven Data Engineer with a 3.94 GPA and hands-on experience building and optimizing scalable ETL pipelines in AWS and hybrid environments.

I excel at implementing data quality frameworks and consolidating pipelines, with expertise in Python, SQL, AWS services, and Apache Hop, and a track record of delivering automated dashboards and cost efficiencies across fintech and tech teams.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Work Experience

Credit Risk Data Analyst at PT Bank Jago Tbk, Jakarta

September 1, 2025 - Present

Propensity Modeling & EWS Integration: Developed dual-segment propensity models for 'New Offer' vs. 'Carry Over' customers, cross-referencing results with the bank's Early Warning System to reduce default risk. Digital Lending Dashboarding: Built automated Looker Studio dashboards to monitor Loan Take-Up Rates and Propensity Scores across 299,000+ customer offers, tracking IDR 9.2B+ in potential disbursements. Collection Strategy Optimization: Conducted delinquency segmentation to classify 'Contactable' vs. 'Uncontactable' customers, enabling prioritization of reachable debtors. Regulatory-Compliant Innovation: Led the pilot Gemini LLM-based OCR for automated bank statement extraction, ensuring strict adherence to OJK compliance standards.

Data Engineer at Mash Technology

September 1, 2025 - Present

Greenfield Data Warehouse Architecture: Designed and built a scalable Data Warehouse from the ground up to centralize scattered business data, establishing the company’s first unified source of truth for analytics. Web Scraping & Schema Standardization: Engineered robust Python-based daily web scrapers to extract pricing data from major Jordanian electronics retailers, implementing automated cleaning scripts to map unstructured HTML data into a strict relational database schema. Pipeline Automation: Automating the ETL process to ensure data freshness for daily competitive analysis reports.

Data Engineer at Insignia.co.id

May 1, 2025 - September 1, 2025

Pipeline Consolidation: Reduced maintenance overhead by 90%+ by consolidating 17 separate Excel ETL workflows into a single dynamic Apache Hop pipeline using Metadata Injection. Cost Optimization: Migrated high volume datamarts from Amazon Redshift to PostgreSQL, significantly lowering monthly cloud billing costs while maintaining query performance. Data Quality Framework: Achieved a 99.5% data quality score by implementing an automated validation framework that checks for freshness, completeness, and uniqueness before loading data into the warehouse. Processing Speed: Optimized PySpark transformations to reduce end to end processing time for financial transaction logs from 4 hours to 90 minutes (62% reduction).