Aletia Trepte

// Respect reduced motion preference
const prefersReducedMotion = window.matchMedia(
  '(prefers-reduced-motion: reduce)'
).matches;

if (!prefersReducedMotion) {
  animateBorder();
}

• 🧩 Architecture: Built and modified Lit-based web components integrated with CMS-driven content.
• ✨ UI / Styling: Implemented responsive UI features using Tailwind CSS and Eleventy (11ty).
• 🌐 Accessibility: Debugged cross-browser rendering issues and accessibility behaviors (ARIA, keyboard navigation).

# Save job settings to S3
          key = f"backup_jobs/job_{job_id}_{today}.json"

          s3.put_object(
            Bucket=bucket_name,
            Key=key,
            Body=json_data,
            ContentType="application/json"
          )

• ⚙️ Automated Recovery: Rebuilds Databricks jobs via REST API with latest valid configurations.
• 🔍 Architecture: Backed up comprehensive job metadata to AWS S3, securely managing edge cases.
• 🚀 Impact: Drastically reduced devastating pipeline disaster recovery times from hours to mere minutes.

import 'dotenv/config';
          await fetch(`https://parseapi.back4app.com/classes/TestClass`, {
            method: 'DELETE',
            headers: {
              'X-Parse-Application-Id': process.env.APP_ID,
              'X-Parse-REST-API-Key': process.env.REST_KEY
            }
          });

• 🛠 Automation: Dynamically generated Parse schemas and organically populated environments securely via Node.js scripts.
• 🧪 Agile TDD: Engineered targeted teardown scripts to instantly reset and sanitize datasets between robust test runs.
• 🔧 Tech Sandbox: JavaScript (ES Modules), Node.js, REST APIs, and Back4App configuration via Environment Variables.

# Simulate freight event stream
          Listening on
          projects/dbt-learn-bigquery-444608/subscriptions/freight-sub...

          Received message: {
            "shipment_id": "feb156c6-788a-4a95",
            "status_update": "in transit",
            "last_known_location": "Phoenix, AZ"
          }

• 🔄 Event Streaming: Engineered real-time publisher/subscriber data pipelines utilizing Google Pub/Sub and Python.
• 📦 Data Modeling: Orchestrated complete dbt staging, intermediate, and robust dimensional mart models mechanically.
• 🧪 Mock Generation: Synthesized highly realistic geographic freight-tracking datasets at scale using Faker and Geopy.

// Scala: Watermarking, event-time processing
          val ranked = df.withColumn("row_num", row_number().over(
            Window.partitionBy("security_description", "source_id", "record_id")
              .orderBy(when(col("is_C"), 3)
                      .when(col("is_B"), 2)
                      .when(col("is_A"), 1)
                      .otherwise(0).desc)
          )).filter(col("row_num") === 1)

• ⚙️ Pipeline Overhaul: Re-architected continuous ingestion and data-matching frameworks actively utilizing Scala + SQL.
• 📊 Optimization: Drastically reduced duplicate entries applying aggressive window functions and data watermarks.
• 🧰 Technical Sandbox: Scala, SQL, Delta Tables, Databricks clusters, AWS S3, and JSON Streaming APIs.

class Educator {
  constructor() {
    this.focus = [
      "Explain Systems",
      "Teach",
      "Structure Learning"
    ];
  }
}

• 📚 Core Experience: 12+ years professionally teaching Computer Science and higher-level mathematics.
• 🗣️ Systems Translation: Dedicatedly translating highly complex engineering architecture into widely understandable concepts.
• 🛤️ Developer Curriculum: Building structured technical learning paths and managing core stakeholder communications.