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arsenelupin14/README.md

Ikbar Faiz

Data engineering

Second-year Computer Science student at BINUS University focused on building reproducible data systems for scientific and observational datasets. My work centers on metadata-first ingestion, structured processing, validation, and analysis-ready outputs that transform raw records into reliable downstream artifacts.

I am especially interested in turning messy data into reusable systems through clear ingestion paths, organized metadata, validated outputs, and workflows that can be rerun, audited, and extended over time.

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Focus Areas

  • Data engineering and ingestion workflows
  • Reproducible pipelines and validation-first design
  • Metadata-driven data processing
  • Automation and reliable technical infrastructure

Languages and Tools

Python NumPy Pandas scikit-learn Jupyter AWS Azure Docker Bash Git

Fun Fact

When i order cheese burger, i throw the pickles into random someone bottle.

Pinned Loading

  1. hlsp-mast-metadata-pipeline hlsp-mast-metadata-pipeline Public

    Metadata-first multi-archive ingestion pipeline for High Level Science Products hosted at MAST.

    Python

  2. iris-solar-uv-data iris-solar-uv-data Public

    Reproducible IRIS Level 2 workflow for archive discovery, metadata indexing, per-OBS quicklooks, and duplicate-window ROI audits.

    Python

  3. rubin-sampling rubin-sampling Public

    Baseline workflow for ZTF/Gaia-linked time-series ingestion, standardization, and period-recovery evaluation.

    Python

  4. t-crb-project t-crb-project Public

    Reproducible T CrB observational workflow with clean products, overlap validation, and archive-aware support assets.

    Python