Projects

Data engineering project portfolio

Each project is structured around the problem, data sources, pipeline design, data model, quality checks, and outcome. Draft content is marked until real project repositories and measurements are added.

Draft content

Batch Sales Analytics Pipeline

A draft portfolio project for a batch ETL pipeline that ingests transaction data, validates schemas, and produces daily revenue metrics.

Case Study

Problem

Raw sales files need to be converted into clean, analytics-ready tables that support revenue reporting and trend analysis.

Pipeline

  • Load raw files into a staging area
  • Validate required fields and data types
  • Transform transactions into fact and dimension tables
  • Publish daily revenue and order metrics

Data Quality

  • Not-null checks
  • Duplicate order detection
  • Referential integrity checks
  • Daily row-count checks

Data Sources

CSV transaction exports, Product reference data, Customer reference data

Tech Stack

Python, SQL, PostgreSQL, Docker

Data Model

stg_transactions, dim_customer, dim_product, fact_orders, mart_daily_revenue

Draft content

dbt Analytics Warehouse

A draft analytics engineering project that models raw event and account data into tested warehouse layers.

Case Study

Problem

Analytics teams need reliable, reusable models instead of repeating transformation logic across notebooks and dashboard queries.

Pipeline

  • Ingest raw source tables into warehouse schemas
  • Create staging models with consistent naming and types
  • Build intermediate models for business logic
  • Expose mart tables for product and revenue analytics

Data Quality

  • Unique key tests
  • Accepted values tests
  • Relationship tests
  • Freshness checks

Data Sources

Application event logs, Account records, Subscription records

Tech Stack

dbt, SQL, BigQuery, Git

Data Model

stg_events, stg_accounts, int_active_accounts, fact_events, mart_account_activity

Draft content

Airflow Data Quality Workflow

A draft orchestration project that schedules pipeline tasks and records validation results for operational visibility.

Case Study

Problem

Data pipelines need repeatable scheduling, failure handling, and visible quality checks before data is trusted downstream.

Pipeline

  • Extract data on a schedule
  • Run transformation jobs in dependency order
  • Execute validation tasks before publishing marts
  • Notify on failed checks or stale data

Data Quality

  • Freshness checks
  • Null-rate thresholds
  • Schema validation
  • Failed-check logging

Data Sources

API extracts, Warehouse tables, Validation rule configuration

Tech Stack

Airflow, Python, SQL, Docker, PostgreSQL

Data Model

raw_api_events, stg_api_events, dq_check_results, mart_valid_events