Data Engineering Master Program
Foundations → Scale & Pipelines → Modern Data Engineering & Architecture
A complete Data Engineering learning journey covering SQL, Python, ETL, Big Data, Spark, Databricks, Airflow, Kafka, dbt, AWS, data quality, observability and data architecture — with practical projects, an end-to-end capstone and interview preparation.
One Complete Data Engineering Journey
Data Engineering is much bigger than learning a collection of tools. You need to understand how data is queried and processed, how pipelines are built and orchestrated, how large-scale systems work, how modern ELT is implemented, how data quality is maintained, and how reliable data platforms are designed. This Master Program brings those skills together in one structured learning journey — starting from the fundamentals and progressing toward modern Data Engineering, architecture and real-world project work.
Why Learn the Complete Journey?
One structured progression
Instead of jumping between disconnected tools, follow a logical progression from fundamentals to large-scale distributed systems and modern cloud architectures.
Full-lifecycle data engineering workflow
Master the complete workflow from extraction and processing to orchestration, transformation, validation, and platform architecture.
Progress from fundamentals to scale
Establish strong foundations in SQL, Python, and ETL before advancing into distributed compute with Spark, Databricks, Airflow, and Kafka.
Master modern data engineering
Learn modern cloud practices with dbt, AWS, automated data quality checks, pipeline testing, monitoring, and observability.
Integrated end-to-end capstone
Stitch together ingestion, storage, Spark/dbt transformation, testing, and monitoring into a comprehensive production capstone.
Technical interview preparation included
Dedicated interview preparation sessions help you communicate architecture choices, solve SQL problems, and explain projects with confidence.
Who Is This Program For?
- Students and fresh graduates wanting a comprehensive, structured curriculum in data engineering
- Beginners entering data engineering who want to start from fundamentals and reach production competence
- Software developers and backend engineers transitioning into data systems and distributed computing
- Working professionals who want to synthesize disconnected data skills into a unified engineering framework
- Aspiring data engineers who want to avoid piecing together separate courses and follow one complete roadmap
Starting Requirements
No prior Data Engineering knowledge required. The program starts from foundational concepts.
12-Week Learning Journey
Progress through three structured stages from foundational data processing to enterprise Big Data and modern cloud architecture.
Data Engineering Foundations
SQL · Python · ETL · Linux · Git · Data Warehousing
SQL Foundations for Data Engineering
- •Relational database fundamentals and data types
- •Filtering, conditions, NULL handling, and pattern matching
- •Aggregations: GROUP BY, HAVING, and summary calculations
- •Multi-table joins: INNER, LEFT, RIGHT, FULL, and self joins
- •Analytical business queries and reporting data subsets
Advanced SQL & Python Foundations
- •Subqueries and Common Table Expressions (CTEs)
- •Window functions: ROW_NUMBER, RANK, DENSE_RANK, LEAD, LAG
- •SQL interview patterns: deduping, running totals, top-N queries
- •Python syntax, control flow, data structures, and functions
- •Virtual environments, package managers (pip), and Python tooling
Python Data Processing & ETL Pipelines
- •File handling: CSV, JSON, Parquet parsing and validation
- •API interaction: Requests, pagination, error handling, retries
- •Building modular, reusable ETL scripts from scratch
- •Idempotency, incremental loading, change data capture (CDC), backfills
- •Introduction to the Medallion architecture (Bronze, Silver, Gold)
Linux, Git & Data Warehousing Fundamentals
- •Linux terminal commands, file manipulation, and shell environments
- •Bash scripting and automated job execution with Cron
- •Git version control: branching, commits, pull requests, merges
- •Data warehousing principles: OLTP vs. OLAP distinctions
- •Dimensional modeling: Star schema, Snowflake schema, Fact & Dimension tables
- •Slowly Changing Dimensions (SCD Type 1 and Type 2) implementation
Big Data & Pipeline Engineering
Spark · Databricks · Airflow · Kafka · Streaming
Modern Data Engineering with dbt & Data Architecture
dbt · AWS · ELT · Data Quality · Observability · Architecture · Capstone
Progressive Practical Work & Final Capstone
Build and work with practical applications directly derived from our curricula.
Foundation Workflows (Weeks 1–4)
SQL analytical drills, automated Python CSV/JSON parser, API ingestion script, and star schema dimensional warehouse model.
Big Data & Pipeline Work (Weeks 5–8)
Spark large-dataset transformation with partition tuning, Databricks ETL workflow, orchestrated multi-stage Airflow DAG, and Kafka streaming simulation.
Modern Data Platform Projects (Weeks 9–11)
Production dbt ELT pipeline with automated tests and snapshots, AWS S3 cloud data warehouse modeling, and data observability suite.
End-to-End Capstone Project (Week 12)
A comprehensive production-grade data engineering solution integrating raw ingestion, AWS storage, Spark processing, dbt transformation, quality gates, and observability.
End-to-End Data Engineering Capstone
Bring together the technical capabilities developed across all 12 weeks to design, build, and deploy an end-to-end data platform that mirrors real-world enterprise infrastructure.
Integrated Architecture Workflow
Why Master Program Instead of Individual Modules?
One Structured Progression
Individual modules are focused, but the Master Program provides one continuous learning journey without needing to guess what to study next.
Connects the Concepts
Learn how SQL, ETL, Spark, Airflow, dbt, AWS, and architecture connect into one cohesive system rather than studying each tool in isolation.
Culminates in an End-to-End Capstone
Move from isolated technical tasks toward building and demonstrating a unified production-grade data platform.
Includes Interview Preparation
Week 12 features dedicated interview readiness to help you explain distributed architectures, data modeling, and trade-offs clearly.
Learn at Your Own Pace
Everything You Need for the Journey
Learn From Industry Experience
Learn from industry professionals with 12+ years of industry experience.
You're Not Learning Alone
Become part of the TailorTech WhatsApp Community and stay connected with fellow learners and the TailorTech team.
*TailorTech does not promise 24/7 support, guaranteed response time, personal mentoring, or 1-on-1 private doubt solving unless formally announced.
The Complete Data Engineering Journey
Register your interest or speak with our advisors to learn about upcoming cohorts and updates.
- 3 complete modules
- ~120-hour curriculum
- Lifetime access
- Practical projects
- End-to-end capstone
- Training materials
- Interview preparation
- Community support
Frequently Asked Questions
Related Programs
Data Engineering Foundations (Module 1)
Standalone module: SQL, Python, ETL, Linux, Git, and Warehousing fundamentals.
Big Data & Pipeline Engineering (Module 2)
Standalone module: Spark, Databricks, Airflow orchestration, and Kafka streaming.
Modern Data Engineering with dbt (Module 3)
Standalone module: dbt transformations, AWS, data quality, and architecture.
Build the Complete Data Engineering Skill Set
Start with the fundamentals. Learn to build pipelines at scale. Master modern Data Engineering, cloud, reliability and architecture. Then bring it all together in an end-to-end project.