Alvin Hans

Reliable By Design

Building the pipelines behind reliable reporting, analysis, and decisions.

I build reusable data-processing workflows, validate SQL transformations, and shape analytics-ready datasets with Python, SQL, PostgreSQL, MySQL, and dbt.

Data Engineering · ETL/ELT · SQL · Python · BI Delivery

Selected WorkGitHub

Selected Work

Data Engineering / Batch Pipeline

Retail Price Intelligence Pipeline

Built a traceable batch pipeline that turns product pages from four retailers into validated, analytics-ready price history.

Proof: 4 retailers / 8 DAG branches / 49 pytest + 12 dbt checks
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Airflow coordinates eight source-category branches while Bronze, Silver, and Gold layers preserve evidence, enforce typed transformations, and publish dbt marts.

Python /Airflow /dbt
Inspect Pipeline
Data & Retrieval Engineering

Hybrid Search Recommendation API

Built a reusable retrieval workflow that prepares, indexes, and combines lexical and semantic signals into one usable API surface.

Proof: NDCG@10 0.798
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A staged data and retrieval flow made intent, relevance, and latency easier to evaluate than a single search method.

Python /FAISS /PyTorch
Inspect Architecture
Data Engineering for BI

Customer Segmentation Analytics

Prepared retail transaction data through reusable processing workflows that supported segmentation and planning decisions.

Proof: 8M+ tx / 2M+ customers
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Clear transformations and interpretable RFM features made the downstream segmentation workflow easier to trust and use.

Python /Scikit-Learn /SQL
See Results
Data Pipeline for Applied NLP

Fintech Review ABSA

Structured Indonesian fintech reviews through a preprocessing and evaluation workflow before the downstream aspect-level NLP layer.

Proof: LoRA + baseline workflow
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A clear risk, trust, and service schema preserved useful downstream signals that one polarity label would hide.

Python /Transformers /Streamlit
View Evidence

Capability Snapshot

Data Engineering

Retrieval Data Pipeline

Built a staged workflow for preparing, indexing, and serving search data, with retrieval quality and latency evaluated downstream.

BM25 + IndoBERT + FAISS, NDCG@10 0.798
Data Engineering for BI

Retail Data Workflows at Scale

Prepared large retail datasets through reusable transformations so segmentation and planning outputs could be generated consistently.

8M+ transactions, 2M+ customer profiles, 70K+ SKUs
Business Intelligence

Validated Reporting Foundations

Validated operational records, standardized KPI logic, and shaped trusted data flows for recurring Power BI reporting.

Power BI across 4 departments, 100K+ records

Downstream Applied Data Science

Review Data Preparation

Prepared and reconciled review data for a downstream NLP workflow that surfaces risk, trust, and service signals.

Downstream Applied Data Science
Risk / Trust / Service, Baseline + PEFT, Streamlit

Experience

Data Science Intern

PT. Matahari Putra Prima Tbk (Lippo Group) | Sep 2025 - Feb 2026

Key Impact
  • ->Developed reusable Python automation modules for 4 recurring retail data workflows
  • ->Structured customer, product, and KPI processing across 8M+ transactions and 2M+ customers
  • ->Extended retail intelligence to 70K+ SKUs for downstream segmentation and demand analysis
What I Did
  • Python-based data processing, transformation, and reusable automation workflows
  • Worked with PostgreSQL and MySQL to prepare and validate retail data for internal use
  • Delivered Streamlit tools and downstream ML/search applications from analytics-ready data

Data Visualization Intern

APP Group (Sinarmas Group) | Jan 2025 - Jul 2025

Key Impact
  • ->Integrated and validated 100K+ operational records with SQL for data quality checks
  • ->Encoded KPI definitions and reporting rules gathered from stakeholders across 4 departments
  • ->Consolidated approximately 8 departmental outputs into a standardized Power BI workflow
What I Did
  • SQL-based data extraction, validation, transformation, and reporting workflows
  • Documented source data, transformation logic, KPI rules, pipeline flows, and dashboard dependencies
  • Translated cross-functional BI requirements into standardized Power BI reporting delivery

Certifications

IBM certification logo

IBM Data Engineering Professional Certificate

IBM In progress

HackerRank certification logo

HackerRank SQL (Intermediate)

HackerRank Issued Aug 2026

PASAS Institute certification logo

Certified International Specialist Data Modelling (CISDM)

PASAS Institute Issued Mar 2026

About

I build reliable data pipelines that turn operational data into trusted reporting and decision-ready systems.

My focus is reusable processing, validated SQL transformations, analytics-ready datasets, and clear handoffs from data foundations to BI delivery.

Applied analytics and ML appear downstream, where they help search, customer intelligence, and operational decisions without obscuring the data work underneath.

Core Stack
Python / SQL / ETL/ELT / Data Pipelines / Data Validation / Data Warehousing / Dimensional Modeling / dbt / PostgreSQL / MySQL / Power BI
Best Fit
Data Engineering / Business Intelligence / Applied Data Science (downstream)

What I Focus On

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Data engineering: Building reusable ETL/ELT workflows around processing, validation, warehousing, and practical constraints.
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Business intelligence: Connecting validated data and KPI logic to Power BI reporting that stakeholders can inspect and use.
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Data at scale: Preparing 8M+ transactions, 2M+ customers, and 70K+ SKUs for reliable downstream analysis.
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Downstream modeling: Using retrieval, segmentation, and NLP only after the data flow and evaluation logic are clear.
Final Notes

Data Engineering, Business Intelligence,and Applied Data Science.

Project context and contact details are below.

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