{
  "meta": {
    "name": "Jakob Gabriel",
    "title": "Digital Business Value Engineer",
    "tagline": "Driving digital transformation through strategic technology implementation and data-driven solutions.",
    "email": "contact@jakobgabriel.com",
    "github": "https://github.com/jakobgabriel",
    "linkedin": "https://linkedin.com/in/jakobgabriel"
  },
  "projects": [
    {
      "id": "uc-cl-mo",
      "title": "Use Case Sprint",
      "type": "Dashboard Mockup",
      "category": "Manufacturing",
      "status": "active",
      "context": "Ongoing Activity",
      "role": "Lead Engineer",
      "year": 2026,
      "description": "As part of engineering rollouts and optimization cycles, I translate operational challenges into abstracted dashboard mockups — building a shared language between shop floor and stakeholders.",
      "opportunity": "During rollouts and optimization cycles, new monitoring and operational needs surface — but translating shop-floor complexity into stakeholder-ready requirements is slow and error-prone without a visual reference.",
      "approach": "I capture each need as an abstracted interactive mockup, stripping away proprietary context while preserving the core challenge. This creates a growing reference of patterns across thermal processing, production tracking, energy management, quality control, and inventory oversight.",
      "outcome": "Each mockup serves as a communication tool: aligning teams, scoping requirements, and accelerating decision-making before implementation begins.",
      "highlights": [
        "Rooted in day-to-day engineering, rollout, and optimization work",
        "Translates operational needs into stakeholder-ready wireframes",
        "Growing reference across 5 manufacturing domains",
        "Used to align teams and scope requirements before implementation"
      ],
      "url": "https://jakobgabriel.github.io/uc-cl-mo/"
    },
    {
      "id": "ts-shape",
      "title": "Timeseries Shaper",
      "type": "Python Library",
      "category": "Data Engineering",
      "status": "active",
      "context": "Open Source",
      "role": "Creator & Maintainer",
      "year": 2024,
      "description": "Python library for loading, shaping, and analyzing time series data. Published on PyPI.",
      "opportunity": "Working with industrial time series data involves repetitive boilerplate — loading from diverse sources, reshaping, feature extraction, and event detection — with no lightweight, composable toolkit available.",
      "approach": "Built a DataFrame-in, DataFrame-out Python toolkit with modular building blocks for loading, transforming, extracting features, and detecting events across timeseries data from Parquet, S3, Azure, and TimescaleDB.",
      "outcome": "Published on PyPI as ts-shape, enabling vectorized, performance-aware processing and reusable event detection for quality, production, and maintenance patterns.",
      "highlights": [
        "Published on PyPI as ts-shape",
        "Supports Parquet, S3, Azure Blob Storage, and TimescaleDB loaders",
        "Vectorized operations for performance-aware processing",
        "Event detection for quality, production, and maintenance patterns"
      ],
      "url": "https://jakobgabriel.github.io/ts-shape/"
    }
  ]
}