Engineering Portfolio // 02

Data Engineering & Cloud Analytics

Building high-velocity pipelines and scalable Lakehouse architectures. From real-time Kinesis streaming on AWS to AI-integrated data mesh environments on GCP and Databricks.

AWS Streaming & Serverless

  • Real-time Ingestion: Amazon Kinesis Streams & Data Firehose.
  • Serverless Processing: AWS Glue, Athena, and Kinesis Analytics for Java.
  • BI & Visualization: Amazon QuickSight integration for actionable insights.
  • Hadoop Ecosystem: AWS EMR and Hadoop fundamentals for distributed processing.

GCP Warehouse & Data Mesh

  • Pipeline Operations: Serverless Data Processing with Google Cloud Dataflow.
  • Warehouse Architecture: Enterprise scaling with BigQuery and Dataplex Data Mesh.
  • AI Integration: Leveraging Gemini Models and Gemini in BigQuery for enhanced productivity.
  • Unified Governance: Secure data management across multi-cloud environments.

Automation & Pipeline Logic

  • Python Mastery: Robust data manipulation and transformation using Pandas.
  • Shell Scripting: Advanced Linux/Bash for workflow automation and file system management.
  • CI/CD for Data: Databricks Asset Bundles and DevOps essentials for data pipelines.
Core Engine

Databricks Delta Lakehouse

Specialized in Apache Spark and Delta Lake architectures to unify data engineering, data science, and analytics in one open environment.

Apache Spark Streaming Delta Live Tables (DLT) Unity Catalog Spark Performance Tuning Medallion Architecture
  • Delta Lake Ingestion: Building resilient, ACID-compliant storage.
  • Stream Processing: Analysis and monitoring of Spark workloads on Databricks.
  • Data Governance: Implementing centralized access control via Unity Catalog.
  • Optimization: Advanced workload monitoring and performance tuning.
Foundation // Database Architecture

The Storage Engine.

Expertise in both Relational and NoSQL paradigms, focusing on high availability, sharding, and deep system configuration.

MongoDB Cluster Ops

  • Advanced CRUD: Sophisticated data manipulation and Indexing strategies.
  • The Pipeline: Complex data transformation via the Aggregation Framework.
  • Distributed Systems: Deployment of Replication sets and Sharded clusters for horizontal scaling.

Oracle Systems Architecture

BCIT // Oracle Workforce Development

  • Database Internals: Managing Initialization files, Redo logs, and Control files.
  • Segment Management: Creating and optimizing Table Spaces and Database Segments.
  • Integrity: Structural management of Oracle database objects and user security.
Verified Transcripts

Credential Clusters & Paths

Google Data Engineer Learning Path

Dataflow | BigQuery | Dataplex | Gemini AI

Dataflow: Foundations

Dataflow: Pipelines

Dataflow: Operations

BigQuery Warehouse

Data Lakes and Warehouse

Gemini in BigQuery

Gemini Models in BQ

AWS Data Analytics Learning Plan

Kinesis | Athena | QuickSight | IoT Analytics

Kinesis Analytics

Amazon Athena

Amazon QuickSight

AWS IoT Analytics

Hadoop Fundamentals

Analyzing Streaming Data

Databricks Data Engineering Learning Path

Delta Lake | Workflows | Unity Catalog

Data Ingestion with Delta Lake

Databricks Workflows

Delta Live Tables

Unity Catalog Governance

DevOps Essentials

Databricks Apache Spark Learning Path

Spark Core | Streaming | Optimization

Python for Data Science/Eng

Introduction to Apache Spark

Developing Spark Apps

Stream Processing & Analysis

Monitoring & Optimization

Core Engineering Foundations

MongoDB: Cluster Administration

MongoDB: Advanced CRUD & Pipeline

Oracle DB Architecture (BCIT)

AWS: Data Analytics (LinkedIn)

Python and Pandas for Data Eng

Linux and Bash for Data Eng