Industry Skills Classroom Training

Analytics Engineer

This course enables you to walk out not just with knowledge — but with the confidence, skills, and real project experience to land an Analytics Engineer role. You will be fully prepared to build data pipelines, transform raw data with dbt, orchestrate workflows with Airflow, ensure data quality, and deliver trusted analytics-ready datasets — ready to perform from Day 1 on the job.

5.0 (2 ratings)
7 enrolled 6 Months | 4 hours/day Professional Tamil

What You'll Learn

  • Master SQL, Python & Linux for Analytics Engineering
  • Build & Manage End-to-End Data Pipelines
  • Transform Raw Data into Trusted Analytics-Ready Models with dbt
  • Orchestrate & Schedule Workflows with Apache Airflow
  • Deliver Reliable Data to Business Teams with Quality & Testing
  • Go from Job Seeker to Job-Ready Analytics Engineer

Requirements

  • Basic Computer Operating Skills
  • No need of previous programming knowledge
  • Suitable for both IT and Non-IT domains

Tools & Technologies

Linux Linux
Bash Bash
Python Python
PostgreSQL PostgreSQL
SQL Alchemy SQL Alchemy
dbt Core dbt Core
Apache Airflow Apache Airflow
Great Expectations Great Expectations
Google BigQuery Google BigQuery
Power BI Power BI
GitHub GitHub
Snowflake Snowflake

Course Description

A professionally designed, industry-aligned classroom program built exclusively for job seekers who are serious about breaking into the Analytics Engineering field. This course bridges the gap between data engineering and data analysis — training you to build clean, reliable, and well-modeled data pipelines that businesses can trust and act on — giving you the confidence to walk into any company and deliver from your very first day at work.

Right for You?

Who This Course is For

  • Fresh Graduates Looking to Start Their Career in Analytics Engineering
  • Job Seekers Who Want to Build Real Data Pipeline & Transformation Skills
  • Candidates Who Prefer Structured Classroom Learning with Mentor Support
  • Anyone Ready to Commit and Become a Job-Ready Analytics Engineer

Course Curriculum

16 modules • 145 lessons
Module 1: Linux & Command Line Basics 10 lessons
  • Introduction to Linux & Why It Matters for Analytics Engineers
  • Linux File System & Directory Structure
  • Terminal Navigation & File Operations
  • File Permissions & User Management
  • Text Processing Commands - grep, awk, sed & cut
  • Shell Scripting Basics with Bash
  • Environment Variables & PATH Configuration
  • Package Management with apt & pip
  • SSH & Remote Server Basics
  • Module 1 Clearance Test
Module 2: Python for Analytics Engineer 12 lessons
  • Introduction to Python & Environment Setup
  • Python Syntax & Data Types
  • Variables, Operators & Expressions
  • Conditional Statements & Loops
  • Functions & Modules
  • Lists, Tuples, Sets & Dictionaries
  • File Handling & Exception Handling
  • Object Oriented Programming
  • Writing Python Scripts for Data Tasks
  • Working with JSON & YAML Files
  • Python Interview Preparation
  • Module 2 Clearance Test
Module 3: SQL for Analytics Engineer 16 lessons
  • Introduction to Databases & PostgreSQL
  • SQL Syntax & Query Structure
  • Filtering & Sorting Data
  • Aggregate Functions & Grouping
  • Join Operations
  • Subqueries & Nested Queries
  • Conditional Logic & CASE Statements
  • Window Functions
  • Common Table Expressions (CTEs)
  • String, Date & Numeric Functions
  • Data Cleaning & NULL Handling
  • Views, Stored Procedures & Indexes
  • Query Optimization & Execution Plans in PostgreSQL
  • Writing Analytical Queries for Reporting
  • SQL Interview Preparation
  • Module 3 Clearance Test
Module 4: Database & Data Warehouse Concepts 8 lessons
  • Introduction to Databases - Relational & Non-Relational
  • OLTP vs OLAP Systems
  • Introduction to Data Warehousing
  • PostgreSQL Advanced Setup & Configuration
  • Data Lake vs Data Warehouse vs Data Lakehouse
  • Introduction to Cloud Data Warehouses
  • Google BigQuery - Architecture & Concepts
  • Module 4 Clearance Test
Module 5: Data Pipeline Concepts 8 lessons
  • Introduction to Data Pipelines
  • ETL vs ELT - Concepts & Differences
  • Batch Processing vs Stream Processing
  • Data Pipeline Architecture & Components
  • Data Flow - Source to Destination
  • Pipeline Design Best Practices
  • Real-World Pipeline Use Cases in South Indian Companies
  • Module 5 Clearance Test
Module 6: Data Extraction & Ingestion 9 lessons
  • Introduction to Data Ingestion
  • Reading & Writing Data with Python & Pandas
  • Connecting to PostgreSQL with Python
  • Extracting Data from REST APIs with Python
  • Handling Pagination & Authentication in APIs
  • Reading Data from CSV, Excel, JSON & XML
  • Incremental Data Extraction Techniques
  • Loading Data into PostgreSQL & BigQuery
  • Module 6 Clearance Test
Module 7: Data Transformation with dbt 13 lessons
  • Introduction to dbt & Analytics Engineering Workflow
  • Setting Up dbt Core with PostgreSQL
  • dbt Project Structure & Configuration
  • Building dbt Models - Sources, Staging & Marts
  • dbt Model Materializations - Table, View, Incremental & Ephemeral
  • dbt Refs & Sources
  • dbt Macros & Jinja Templating
  • dbt Tests - Generic & Singular Tests
  • dbt Documentation & Data Lineage
  • dbt Seeds & Snapshots
  • Running & Debugging dbt Models
  • Connecting dbt to Google BigQuery
  • Module 7 Clearance Test
Module 8: Data Modeling for Analytics 8 lessons
  • Introduction to Dimensional Modeling
  • Star Schema & Snowflake Schema Design
  • Fact Tables & Dimension Tables
  • Slowly Changing Dimensions (SCD)
  • Wide Tables vs Normalized Models
  • Kimball vs Inmon Methodology
  • Building Analytics-Ready Data Models with dbt
  • Module 8 Clearance Test
Module 9: Data Quality & Testing 8 lessons
  • Introduction to Data Quality & Why It Matters
  • Data Quality Dimensions - Accuracy, Completeness & Consistency
  • dbt Built-in Tests - Not Null, Unique, Accepted Values & Relationships
  • Writing Custom dbt Tests
  • Introduction to Great Expectations
  • Building Expectation Suites with Great Expectations
  • Data Validation & Alerting Strategies
  • Module 9 Clearance Test
Module 10: Workflow Orchestration with Airflow 10 lessons
  • Introduction to Workflow Orchestration
  • Apache Airflow Architecture & Components
  • Setting Up Airflow Locally
  • Understanding DAGs - Directed Acyclic Graphs
  • Writing & Scheduling DAGs
  • Airflow Operators - Python, Bash & SQL
  • Task Dependencies & XComs
  • Monitoring & Debugging Airflow DAGs
  • Integrating dbt with Airflow
  • Module 10 Clearance Test
Module 11: Data Visualization & Reporting with Power BI 7 lessons
  • Introduction to Power BI for Analytics Engineers
  • Connecting Power BI to PostgreSQL
  • Connecting Power BI to Google BigQuery
  • Building Reports on Analytics-Ready dbt Models
  • Designing Dashboards for Business Teams
  • Publishing & Sharing Reports via Power BI Service
  • Module 11 Clearance Test
Module 12: Cloud Data Platform - Google BigQuery 8 lessons
  • Introduction to Google Cloud & BigQuery
  • BigQuery - Free Tier Setup & Console Navigation
  • Running SQL Queries in BigQuery
  • Loading Data into BigQuery
  • BigQuery Partitioning & Clustering
  • Connecting dbt Core to BigQuery
  • Cost Optimization & Query Best Practices in BigQuery
  • Module 12 Clearance Test
Module 13: Snowflake for Analytics Engineers 12 lessons
  • Introduction to Snowflake & Its Architecture
  • Snowflake vs Traditional Data Warehouses
  • Snowflake Free Trial Setup & Console Navigation
  • Snowflake Objects - Databases, Schemas, Tables & Views
  • Virtual Warehouses & Compute Scaling
  • Roles & Access Control in Snowflake
  • Running SQL Queries in Snowflake
  • Loading Data into Snowflake
  • Snowflake Time Travel & Data Cloning
  • Connecting dbt Core to Snowflake
  • Running dbt Models on Snowflake
  • Module 13 Clearance Test
Module 14: Version Control & Project Structure 7 lessons
  • Introduction to Git & GitHub
  • Git Basics - Clone, Commit, Push & Pull
  • Branching, Merging & Pull Requests
  • Analytics Engineering Project Folder Structure
  • Writing README & Documenting Analytics Projects
  • Building a GitHub Portfolio for Analytics Engineering
  • Module 14 Clearance Test
Module 15: Analytics Engineering Best Practices 7 lessons
  • Naming Conventions for Models, Columns & Files
  • Modular & Reusable Code Design
  • Documentation Standards in Analytics Engineering
  • Code Review & Collaboration Best Practices
  • Building Trusted Data Products for Business Teams
  • Analytics Engineering in Real Company Environments
  • Module 15 Clearance Test
Module 16: Projects 2 lessons
  • Mentored Project
  • Capstone Project
Our Advantage

Why Choose This Course

  • Built Exclusively for Job Seekers
  • Only Course That Covers dbt & Airflow in a Classroom
  • Classroom Training with Real Mentor Guidance
  • Build a Portfolio That Gets You Hired

Your Instructors

Leelavathi Ramesh

Leelavathi Ramesh

Business Intelligence Analyst

9 Courses

4+ years of experience as a Business Intelligence Analyst, with hands-on expertise at Variablz Technology and Schneider Electric. Statistics graduate with practical experience across Marketing, Production, and Sales Analytics.

Parthiban Kannan

Parthiban Kannan

Co-founder & Manager

17 Courses

14+ years of experience in digital transformation, leading teams of 15+ members and delivering 160+ projects for organizations like Lakshmi Machine Works, Milacron, Schneider Electric, Aatomz Research, and Variablz Technologies. AI innovator with a patent approved by the Government of India, with strong expertise in Data Science, Data Analysis, Business Intelligence, and Software Development.

Course Outcomes

What Our Students Build

Articles, projects, research papers and more — real work by our learners applied beyond the classroom.

View All Outcomes
FAQ

Frequently Asked Questions

Is this course suitable for freshers with no data engineering experience?

Yes. This course starts from Linux and Python basics and gradually takes you to advanced Analytics Engineering tools like dbt and Airflow u2014 no prior experience is required to join.

What are the eligibility criteria to join this course?

Any graduate from any degree is eligible, provided their degree includes Mathematics as a subject.

Is this course suitable for college students or working professionals?

No. This course is exclusively designed for job seekers who are seriously looking to start their career as an Analytics Engineer.

Is this an online or offline course?

This is a completely classroom-based training program conducted at our Cuddalore location.

Will I work on real projects during this course?

Yes. You will work on a Mentored Project with trainer guidance and an independent Capstone Project u2014 both built around real-world data pipeline and transformation scenarios.

What makes this course different from a Data Analyst course?

A Data Analyst focuses on analyzing and visualizing data. An Analytics Engineer focuses on building the clean, reliable data infrastructure that analysts use u2014 working with pipelines, dbt models, data warehouses, and orchestration tools.

Will I learn cloud platforms in this course?

Yes. You will get hands-on exposure to BigQuery and AWS Redshift fundamentals using free tier accounts u2014 giving you enough cloud knowledge to work in real company environments.

What kind of companies can I apply to after this course?

You will be job-ready for product companies, MNCs, startups, and analytics firms across Tamil Nadu and South India hiring for Analytics Engineer, Data Engineer, dbt Developer, and BI Engineer roles.

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