Module 03 of 03

Modern Data Engineering with dbt & Data Architecture

dbt · AWS · ELT · Data Quality · Observability · Architecture

Learn dbt, modern ELT, AWS cloud concepts, data quality, testing, monitoring, observability and data architecture — and bring them together through an end-to-end capstone project.

FormatRecorded Program

Duration~40 Hours

AccessLifetime Access

PrerequisitesRecommended

Status: Currently not live

Overview

Move From Building Pipelines to Building Reliable Data Systems

Modern Data Engineering isn't only about moving data. Data teams also need to transform data using modern ELT practices, test and validate data, monitor pipeline behaviour, understand observability and design scalable data platforms. This module brings these capabilities together through dbt, AWS, modern data quality practices, monitoring, observability and data architecture.

Core Technology Scope

dbt Models, Sources, Tests & Snapshots (Modern ELT)AWS Cloud Infrastructure (S3, EC2, IAM, Redshift / Warehouses)Data Quality Checks, Pipeline Testing & CI/CDMonitoring, Metrics, Alerting & Data Observability (SLAs)Data Architecture (Lakehouse, Event-Driven Patterns)End-to-End Capstone Project & Technical Interview Prep

Key Advantages

Build Reliable Data Systems, Not Just Data Pipelines

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Modern ELT with dbt

Use dbt (data build tool) to version-control, document, and execute transformation models directly inside modern data warehouses.

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AWS cloud infrastructure

Understand the core AWS cloud services (S3 storage, compute, IAM policies, data lakes, and warehouse services) that power enterprise data platforms.

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Data quality & testing

Implement automated data quality checks, schema validations, and pipeline tests before corrupted data reaches downstream consumers.

✓

Monitoring & observability

Track pipeline behavior through metrics, alerting thresholds, and Service Level Agreements (SLAs) for dependable data delivery.

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Modern data architecture

Study modern lakehouse architecture, decoupled storage/compute, and event-driven design patterns.

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End-to-end capstone & interview prep

Consolidate your learning by building a complete data platform capstone and preparing to explain it in technical interviews.

Target Audience

Who Is This Program For?

Aspiring Data Engineers looking to master modern transformation, cloud architectures, and production reliability
Data Engineers who want to strengthen their skills in dbt, AWS, data quality frameworks, and observability
Developers transitioning into data platforms who need to understand how modern cloud data warehouses and lakehouses are engineered
Learners who have completed Modules 1 and 2 or have equivalent background ready for modern data architecture

Prerequisites

Starting Requirements

A basic understanding of SQL, Python, ETL/data pipelines, and data warehousing concepts is recommended. Modules 1 and 2 are recommended pathways, but not mandatory.

Level: Intermediate / Advanced

Curriculum Breakdown

What You'll Learn

Structured modules designed to build competencies systematically.

•Introduction to dbt (data build tool) and why ELT replaced traditional ETL in cloud warehouses

•dbt project structure, dbt_project.yml configuration, and warehouse connection profiles

•Authoring dbt models: Materializations (views, tables, incremental models, ephemeral)

•Defining sources and staging layers (ref function, source function, lineage graphs)

•Data testing in dbt: Generic tests (unique, not_null, accepted_values, relationships) and singular tests

•dbt snapshots for Slowly Changing Dimensions (SCD Type 2)

•Automated documentation generation and dependency graph lineage visualization

Outcome: Transform raw warehouse data into structured, analytics-ready datasets using modern dbt ELT practices.

Real-World Applications

Four Practical Projects & Capstone

Build and work with practical applications directly derived from our curricula.

dbt · SQL · Cloud Warehouse

Production ELT Pipeline with dbt

Develop a multi-layer dbt transformation project with staging, intermediate, and mart models, incorporating automated tests and snapshots.

Focus: dbt ref(), incremental materializations, generic tests, and documentation.

AWS S3 · AWS Redshift · SQL

Cloud Data Warehouse Architecture

Design and implement a scalable, reporting-ready data warehouse schema on AWS with partitioned storage and optimized query models.

Focus: Partitioning strategies, schema design, and analytical query performance.

dbt Tests · Great Expectations · Alerting

Data Quality & Observability Suite

Implement automated data quality test suites, SLA freshness checks, and Slack/email alerting triggers across a data pipeline.

Focus: Data validation, threshold alerts, and SLA monitoring.

AWS · dbt · SQL · Pipeline Architecture

End-to-End Capstone Project

Integrate ingestion, transformation with dbt, automated quality testing, cloud storage, and monitoring into one unified, production-oriented data platform.

Focus: Comprehensive end-to-end data pipeline lifecycle and technical interview presentation.

End-to-End Capstone

Build an End-to-End Data Engineering Solution

Bring together multiple Data Engineering components into one integrated project and develop a production-oriented data solution that you can confidently present during technical interviews.

Integrated Architecture Workflow

1.Data Ingestion from Raw Sources

2.Storage in Scalable Cloud Data Lake (AWS S3)

3.Transformation with dbt Models & Snapshots

4.Automated Data Quality Checks & Testing Gates

5.Pipeline Monitoring, Metrics & SLA Alerting

6.Production-Oriented Lakehouse Architecture

Product Architecture

The Final Stage of the Data Engineering Journey

This module is Module 03 of the 3-module Data Engineering Master Program.

MODULE

View Module

Module 1: Data Engineering Foundations

SQL · Python · ETL · Linux · Warehousing

MODULE

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Module 2: Big Data & Pipeline Engineering

Spark · Databricks · Airflow · Kafka · Streaming

CURRENT MODULEACTIVE

Module 3: Modern Data Engineering with dbt & Architecture

dbt · AWS · ELT · Data Quality · Observability · Architecture

Want all three modules in one unified 12-week progression with Capstone?Explore Master Program

Package Breakdown

Everything You Need for the Journey

~40 Hours of advanced recorded curriculum

Lifetime access to all lectures and updates

Comprehensive slides, architecture templates, and project codebases

dbt project boilerplate and automated test templates

End-to-End Capstone Project guidance

Technical interview preparation guidance

TailorTech WhatsApp Community support

Instructor Experience

Learn From Industry Experience

Delivered by an industry professional with 12+ years of experience across modern ELT, cloud platforms, data observability, and enterprise lakehouse architectures.

Program Status

Inquire About Modern Data Engineering with dbt & Data Architecture

Status

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