Full Stack Data Engineering Course

#1 Full Stack Data Engineering Course
for Working Professionals | 100% Job Guarantee

Full Stack Data Engineering Course for Working Professionals Syllabus plays a crucial role in getting your career transition, but are you learning a job-transition syllabus or beginner Data Engineer syllabus? BEPEC Full Stack Data Engineering Course Makes your Data Engineer Career Transition 100% Possible with the 3 Step correct formula.

Step-1: You will learn Career-Transition Data Engineer Course Syllabus, which includes Python, SQL, Spark, Databricks, Snowflake, Airflow, dbt, Kafka, AWS, Azure, GCP, Data Lakehouse, Delta Lake, Data Modelling, Streaming Pipelines, DataOps, CI/CD & GenAI-powered Data Engineering
Step-2: You will work as an Intern/Freelancer to build a portfolio which is most needed to be an Data Engineer. Finally,
Step-3: Interview Preparation like Resume Building, Mock Interviews, Previous Interview Clips from BEPEC Alumni, Interview Support & Post Placement Support.

Learners

0 +

Data Engineer Tools

0 +

Max Recorded Salary

Days of Internship

Career Transition Checklist

6 Steps to Kick Start Your Career into Data Engineer

Step-1

Step:1 Speak with our Career Advisor and Discuss about your Background. Fill the Scope Analysis Test & Get Personalised Roadmap on Data Engineer.

Step-2

Step:2 Speak with Our Mentor Mr Kanth & Get 1-on-1 Roadmap Discussion Call. Clarify all your Doubts and Confusions before you Kick Start with your Career Transition Journey.

Step-3

Step:3 Start your Data Engineer Learning Journey with Weekday Live Classes. You will learn Python, SQL, Spark, Databricks, Snowflake, Airflow, dbt, Kafka, AWS, Azure, GCP, Data Lakehouse, Delta Lake, Data Modelling, Streaming Pipelines, DataOps, CI/CD & GenAI-powered Data Engineering

Step-4

Step:4 After completing the Syllabus, you must start working on Real-Time Projects to develop Analytical Thinking, Problem Solving & Convincing Skills Ability. For all the projects you completed under BEPEC, you can place them as Internship/Experience in your Resume.

Step-5

Step: 5 After completing the Real-Time Projects, We review your projects and share essential feedback and corrections. We move to the next stage, which includes Mock Interviews & Resume Building with the right roles & responsibilities and projects based on your background.

Step-6

Step:6 Once the Resume got finalised, We push your Resume to our hiring partners, and even you update your Resume across various job portals like LinkedIn, Hirist, Naukri etc.. 1-on-1 Mentorship with Kanth until you crack the interviews and post-placement support.

Data Engineer Key Skills from BEPEC Program

Job-Ready AI Engineer Career Transition Program with 100% Guaranteed Career Transition

Python

Postgres

SQL, Snowflake

Data Warehousing: ETL & ELT

Dbt

PySpark

Databricks

Azure Data Factory

Airflow

Kafka

Medallion Architecture

Big Data, HDFS

Data Lakehouse

Microsoft Fabric

DataOps

AWS

Star Schema, Snowflake Schema

GCP

Delta Lake

Streaming Pipeline

Vector databases

Gen AI

Agents for Data Engineering

Graph databases - Neo4j

NoSQL Database

Docker

Data Engineer System Design

CI/CD

Convincing Skills

Full Stack Data Engineer Career Transition Program with Remote Internship

New Live Weekday Batch Start from 6th July 2026 {8.00PM - 9.30PM}

Early-Bird Offer

Data Engineer End-to-End Projects

Job-Ready Data Engineering Career Transition Program with 100% Guaranteed Career Transition

Legacy On-Premise Data Warehouse Migration to Snowflake (Cloud Modernization)

Objective & Scale: Migration of an enterprise Teradata data warehouse containing over 50 Terabytes of historical transactional records to a modern cloud-native Snowflake Data Platform.
Core Pipeline: Utilized AWS Snowball for physical initial bulk data movement, paired with AWS Schema Conversion Tool (SCT) and custom Python scripts to parse, translate, and re-engineer legacy SQL scripts into Snowflake-compliant SQL.
Orchestration & Target: Automated ongoing daily changes via AWS DMS (Data Migration Service) into Amazon S3, using Snowpipe and Snowflake Streams/Tasks to execute continuous incremental loading and Change Data Capture (CDC).

Real-Time Customer Clickstream & Analytics Ingestion Engine (Streaming Data Architecture)

Objective & Scale: Real-time collection and multi-layered processing of millions of concurrent user clickstream events from global web and mobile applications for live behavior analytics.
Core Pipeline: Implemented an enterprise Apache Kafka cluster deployed on AWS to ingest high-velocity event streams, decoupled through Schema Registry to ensure strong downstream data governance.
Orchestration & Target: Employed PySpark Structured Streaming on Amazon EMR to perform real-time window aggregations and sessionization, routing data directly into Amazon Redshift for live business intelligence dashboards.

Automated Dual-Cloud Data Lakehouse Implementation (Azure & AWS Hybrid)

Objective & Scale: Building a centralized, secure financial transaction repository designed to eliminate data silos between distinct regional offices operating on separate cloud infrastructures.
Core Pipeline: Orchestrated automated cross-cloud extraction using Azure Data Factory (ADF) to ingest disparate data types (Parquet, Avro, and JSON formats) into a unified cloud landing zone.
Orchestration & Target: Engineered complex ETL transformation logic using Azure Databricks (PySpark) to build an optimized Delta Lake architecture, enabling ACID transactions and fast BI querying over multi-terabyte raw data lakes.

E-Commerce Inventory Change Data Capture (CDC) Pipeline (Transactional Data Sync)

Objective & Scale: High-frequency synchronization of critical retail inventory and pricing catalog updates between live production MySQL operational databases and analytics warehouses.
Core Pipeline: Configured Debezium to continuously monitor database binlogs (binary logs), capturing minute schema and row modifications instantly without impacting active production database performance.
Orchestration & Target: Streamed the raw CDC event records directly through Apache Kafka, transforming and upserting the mutations seamlessly into a target Snowflake Data Warehouse using Snowflake Streams.

Healthcare Data Anonymization & Governance Engine (Compliance & Security migration)

Objective & Scale: Security-focused migration of sensitive Patient Health Information (PHI) from relational databases into centralized repositories while remaining strictly compliant with HIPAA and GDPR regulations.
Core Pipeline: Designed an isolated Python-based data cleaning and masking pipeline that automatically scans inbound data, detects sensitive strings, and applies hashing or format-preserving encryption algorithms.
Orchestration & Target: Validated structural layout integrity using Great Expectations before populating encrypted tables inside Microsoft Azure SQL Database, configuring role-based row and column-level security.

Natural Language to Enterprise SQL Agent (With Advanced RAG Guardrails)

Objective: A highly reliable text-to-SQL agent that allows non-technical business stakeholders to query a multi-terabyte data warehouse safely, matching the performance of a human data analyst.

Key Program Highlights

Designed for Working Professionals & Freshers

250+ Hours of Holistic Learning Access for Lifetime

8+ Real-Time Projects to #BuildExperience or Portfolio

1-on-1 Mentorship until you get placed into Job

1-on-1 Mock Interview to #BuildConfidence before you attend interview

Interview Level Data Engineering Projects & Data Engineer System Design

10 + Industry Projects to #BuildConfidence on Data Engineer

Get Course Completion Certificate + Experience Certificate

Learn 15+ Tools related Data Engineer Job Profile

Smart Board Driven Classes to create Classroom kinda environment

Top-Notch Training from Working Senior Data Engineers & AI CoEs

Data Engineer Portfolio Crafting, To Shortlist your Resume & LinkedIn

Essential Soft Skills Training, To Master your interviews & Career

Pre & Post Reading Material with Quiz & Assessments

Lifetime Access for Recorded Classes, If you miss any live class

Live Doubt Resolution Classes on Weekdays by Working Data Engineers

No Cost EMI

You can apply for Data Engineering, Data Analyst, Azure Data Engineer

No-Coding to Job-Ready Data Engineering Course Curriculum

Why Choose BEPEC for Data Engineer Course

100% Practical Portfolio & POCs: Build production-ready architectures, not just standard assignments.
Dual-Cloud Mastery: Gain native expertise in both AWS Data Ecosystems and Microsoft Azure Big Data stacks.
Dedicated Internship & Placements: Tap into our network of 500+ hiring partners globally across India, USA, UAE, and the UK.
Led by: Learn directly from Rajeev Kanth, an AI Solutions Architect & Principal Data Scientist with 13+ years of global corporate training experience for Fortune 500 giants (EY, Nokia, Cognizant, DBS Bank).

Introduction to Data Engineering & Modern Data Stack

What is Data Engineering and its role in the data lifecycle
Data Engineer vs Data Analyst vs Data Scientist vs ML Engineer
Evolution: On-prem warehouses → Hadoop → Cloud → Lakehouse
The Modern Data Stack overview (ingest, store, transform, serve)
Batch vs Streaming paradigms
OLTP vs OLAP systems
Data Warehouse vs Data Lake vs Data Lakehouse
Roles, responsibilities, and career roadmap in Data Engineering
Overview of tools: Spark, Snowflake, Databricks, Airflow, dbt, Kafka
How GenAI is reshaping Data Engineering workflows

Python: Zero to Job-Level

Introduction to Python
Why Python, Value, Variable, Function, Library [Roadmap on Python]
IDE in Python, Different Data Types
List, Tuple, Set & Dictionary Overview
Different List Methods
Different Tuple Methods
Set & Frozenset
Dictionary & String Manipulations
Overview on Loops, If Statements, UDFs, Escape Sequences, Lambda
Types of Operators, Conditional Statements
While Loop, List Comprehension, Break, Continue, Arguments
Functions, Escape Sequences, Lambda Functions
Hackathon-1
Iterators, Decorators & Generators
Modules in Python
Creating Custom Python Libraries
Lambda, Map, Filter, Reduce
Exception Handling
File Handling
Regular Expressions (Regex)
Web Scraping Basics
Introduction to OOPS
Instance Variable, Class Variable, Class Method
Association vs Composition & Aggregation
Oops Concept
Encapsulation, Inheritance
Polymorphism, Method Overloading, Method Overriding
Introduction to Pandas
Data Analysis using Pandas
Introduction to Numpy
Different Numpy Commands
Why Data Cleaning?
Data Cleaning with PySpark, Pandas
Connecting Python to Databases (SQLAlchemy, psycopg2)
Writing Production-Grade Python (logging, config, type hints)
Regular Expression Basics
Mastering Langchains
Mastering Langgraph

Advanced Data Structures & Algorithms

Introduction to Advanced DSA
Non-Primitive Data Structures
Non-Linear Data Structures
What is an Algorithm
Theory & Code Implementation of Linked List
Stacks & Queues Assignment
Coding Stack Data Structure
Coding Queue Data Structure
Tree Data Structures
Types of Tree Data Structure
Tree Traversal
BFS Traversal
Bubble Sort Theory
Bubble Sort Code Implementation
Selection Sort Theory
Selection Sort Code Implementation
Insertion Sort Theory
Insertion Sort Code Implementation
Merge Sort Theory
Merge Sort Code Implementation
Quick, Merge Sort Performance
Quick Sort Theory
Linear Search & Bisection Search

AI Python Coding Interview Prep

Pandas DataFrames for Coding Interviews
Data Cleaning and Imputation Logic
GroupBy and Aggregation Coding
Merging, Joining, and Concatenating DataFrames
Reshaping Data (Melt, Pivot)
NumPy Array Broadcasting
Vectorized Operations vs Loops
Handling DateTime Objects in Python
String Manipulation with Regex

Hands-On SQL, Projects with Data Warehouse Concepts

- What is SQL, RDBMS, and Table Structure
- Understanding Sprint, Scrum and Agile Project Breakdown in SQL
- OLTP vs OLAP
- Data Warehousing Concepts
- Data types (INT, VARCHAR, DATE, BOOLEAN, etc.)
- ER Diagrams
- Data Models like Star Schema and Snowflake Schema
- DDL vs DML vs DCL vs TCL Commands
- Basic CRUD Operations — 41:21
- Different DDL Commands — 41:22
- Different DML Commands
- Upsert Operations
- Different DQL Commands
- Database Constraints
- Aggregate Functions, Date Functions and String Functions
- SQL Joins: Inner, Self, Cross, Left, Right and Outer Join
- SQL Grouping & Aggregations
- SubQueries and Types of SubQueries
- Window Functions in SQL

Hands-On Advance SQL Concepts

- Data Integrity & Referential Integrity
- Data Normalisation
- First & Second Normal Form
- Functional Dependency & Transitive Dependency
- Boyce Codd Normal Form
- Denormalization
- Temporary Tables, CTE, Recursive CTE
- When to Use Temporary Table, CTE, Recursive CTE
- SubQuery in MySQL
- Views in MYSQL
- Stored Functions
- Stored Procedures
- Triggers in MySQL
- Create Events
- In-depth DDL Commands
- Different Functions in MySQL

Data Modelling & Warehouse Design

Conceptual, Logical & Physical Data Models
Dimensional Modelling (Kimball Methodology)
Fact Tables vs Dimension Tables
Star Schema vs Snowflake Schema
Slowly Changing Dimensions (SCD Type 0,1,2,3,6)
Surrogate Keys & Natural Keys
Data Vault Modelling (Hubs, Links, Satellites)
Inmon vs Kimball approaches
Designing a Warehouse from Source Systems
Grain, Hierarchies & Conformed Dimensions

File Formats & Data Serialization

Row vs Columnar Storage
CSV, JSON, XML Handling
Parquet — Internals & Why Columnar Wins
Avro & Schema Evolution
ORC Format
Compression Codecs (Snappy, Gzip, Zstd)
Choosing the Right Format for the Right Job

Apache Spark & PySpark (Core)

Introduction to Big Data & Distributed Computing
Hadoop Ecosystem Overview (HDFS, YARN, MapReduce)
Why Spark? Spark Architecture (Driver, Executors, Cluster Manager)
RDDs, DataFrames & Datasets
Transformations vs Actions (Lazy Evaluation)
PySpark DataFrame API — Select, Filter, GroupBy, Join
Spark SQL & Temp Views
Reading & Writing Multiple Formats
Handling Nulls, Schemas & Data Types
User Defined Functions (UDFs) & Pandas UDFs
Window Functions in Spark

Apache Spark: Advanced & Performance Tuning

Spark Internals: DAG, Stages, Tasks, Shuffles
Partitioning, Repartition vs Coalesce
Broadcast Joins & Join Strategies
Caching & Persistence Levels
Catalyst Optimizer & Tungsten Engine
Handling Data Skew
Bucketing & Z-Ordering
Adaptive Query Execution (AQE)
Memory Management & Configuration Tuning
Debugging Spark Jobs via Spark UI

Databricks & Delta Lake (Lakehouse)

Introduction to Databricks Platform & Workspace
Databricks Notebooks, Clusters & Jobs
Delta Lake — ACID Transactions on Data Lakes
The Medallion Architecture (Bronze/Silver/Gold)
Time Travel & Versioning
MERGE, UPDATE, DELETE on Delta Tables
Schema Enforcement & Schema Evolution
OPTIMIZE, VACUUM & Z-Ordering
Auto Loader for Incremental Ingestion
Unity Catalog for Governance
Delta Live Tables (DLT) Pipelines

Snowflake — Cloud Data Warehouse

Snowflake Architecture (Storage, Compute, Cloud Services)
Virtual Warehouses & Auto-Scaling
Databases, Schemas, Tables & Stages
Loading Data (COPY INTO, Snowpipe)
Micro-Partitions & Clustering Keys
Time Travel & Zero-Copy Cloning
Streams & Tasks for CDC
Secure Data Sharing & Marketplace
Snowpark for Python
Cost Management & Resource Monitors
Performance Optimization in Snowflake

dbt — Analytics Engineering & Transformation

What is dbt & the ELT Paradigm
Models, Sources & Seeds
Jinja Templating & Macros
Incremental Models & Materializations
Tests (Generic & Singular) & Data Quality
Snapshots for SCD
Documentation & Lineage Graphs
dbt Packages & Reusability
Deploying dbt with CI/CD
dbt + Snowflake / Databricks / BigQuery

Apache Airflow Orchestration

Why Orchestration? Airflow Architecture
DAGs, Tasks & Operators
Scheduling, Backfilling & Catchup
Task Dependencies & XComs
Sensors, Hooks & Connections
Branching, Trigger Rules & Dynamic DAGs
TaskGroups & SubDAGs
Retries, SLAs & Alerting
Custom Operators & Plugins
Airflow Best Practices for Production
Alternatives Overview (Dagster, Prefect, Mage)

Data Ingestion — Batch & CDC

Ingestion Patterns: Full Load vs Incremental
Change Data Capture (CDC) Concepts
CDC with Debezium & Kafka Connect
API & Webhook Ingestion
File-Based Ingestion (S3/ADLS/GCS Triggers)
Fivetran / Airbyte for Managed Ingestion
Idempotency & Exactly-Once Delivery
Schema Drift Handling

Apache Kafka & Real-Time Streaming

Introduction to Event Streaming
Kafka Architecture: Brokers, Topics, Partitions, Offsets
Producers & Consumers
Consumer Groups & Rebalancing
Kafka Connect & Schema Registry
Exactly-Once Semantics & Idempotent Producers
Spark Structured Streaming Fundamentals
Windowing, Watermarks & Late Data
Stateful Streaming & Checkpointing
Stream-Stream & Stream-Static Joins
Real-Time Pipeline End-to-End

NoSQL & Specialized Stores

SQL vs NoSQL — When & Why
Document Stores (MongoDB)
Wide-Column (Cassandra / HBase)
Key-Value (Redis) for Serving & Caching
Graph Databases (Neo4j) Overview
Time-Series Databases
CAP Theorem & Consistency Models

Cloud Data Engineering — AWS

AWS Core Services for Data Engineers
S3 as a Data Lake & Storage Tiers
AWS Glue (Catalog, ETL Jobs, Crawlers)
Amazon Redshift & Redshift Spectrum
AWS EMR for Spark Workloads
Kinesis for Streaming
Lambda for Serverless ETL
Athena for Serverless Querying
IAM, Security & VPC Basics

Cloud Data Engineering — Azure

Azure Data Lake Storage (ADLS Gen2)
Azure Data Factory (Pipelines, Dataflows, Triggers)
Azure Synapse Analytics
Azure Databricks Integration
Azure Event Hubs & Stream Analytics
Azure Key Vault & Security

Cloud Data Engineering — GCP

Google Cloud Storage
BigQuery Architecture & Optimization
Dataflow (Apache Beam)
Dataproc for Spark/Hadoop
Pub/Sub for Streaming
Cloud Composer (Managed Airflow)

Data Quality, Governance & Observability

Why Data Quality Matters
Great Expectations for Validation
Data Contracts between Producers & Consumers
Data Lineage & Impact Analysis
Data Cataloging (Unity Catalog, DataHub, Amundsen)
Data Observability (freshness, volume, schema, distribution)
PII Handling, Masking & Compliance (GDPR/HIPAA)
Role-Based Access Control

DataOps, CI/CD & Infrastructure

DataOps Principles & Culture
Version Control for Data Pipelines
Testing Data Pipelines (unit, integration, data tests)
CI/CD with GitHub Actions / GitLab CI
Containerization with Docker for DE
Introduction to Kubernetes for Data Workloads
Infrastructure as Code with Terraform
Environment Management (Dev/Stg/Prod)
Monitoring & Alerting (Grafana, Prometheus)
Cost Optimization Strategies

Generative AI, Prompt Engineering using LangChains

What is Generative AI? Use cases in text, image, code, and conversation
Foundation Models: GPT, Claude, PaLM, Gemini, Mistral, LLaMA
Introduction to LangChains
Using LLMs using Lang Chains
What is Prompt Engineering
Prompt Engineering Principles
Zero-shot, Few-shot, Chain-of-Thought (CoT)
Best Way to Improve LLMs Accuracy
Doing Prompt Engineering using Lang Chains
PromptTemplate best practices with LangChain
Using ChatPromptTemplate, SystemMessagePrompt

GenAI for Data Engineering (2026 Edge)

How LLMs Accelerate Pipeline Development
Text-to-SQL Agents on Warehouses
RAG over Data Catalogs & Documentation
Vector Databases (FAISS, Pinecone, pgvector) for DE
Embeddings & Semantic Search on Metadata
Building a Data Assistant with LangChain
Automated Data Documentation with LLMs
AI-Powered Data Quality & Anomaly Detection
LLMs for Schema Mapping & Migration

Capstone: End-to-End Production Pipeline

Ingest from multiple sources (API + DB CDC + Files)
Land raw data into Data Lake (Bronze)
Transform & clean with Spark/dbt (Silver)
Build dimensional model & aggregates (Gold)
Orchestrate with Airflow + data-quality gates
Stream a real-time component via Kafka
Add observability, lineage & CI/CD
Serve to BI + a GenAI Text-to-SQL layer → Deploy on Cloud

250+ Hours of Holistic Learning Content
Motivational Classes
LifeTime Recorded Lectures Access
Hike Guaranteed Program
100+ Tied-up Companies
100% Job Guarantee
Thrice a Week Doubt Clarification Classes
2 Real-Time Projects to Work & Place in Resume
Resume Roles & Responsibilities
Internship Certificate
Learn 15+ Tools

9499INR(Including GST)

Live Weekday Mentor Drive Lectures
Live Classes(Mon-Fri)
3-4 Months Live Classes
1 Year Internship
250+ Hours of Holistic Learning Content
Access Indepth Mindmaps
LifeTime Recorded Lectures Access
System Design Classes & Problem Solving Classes
Hike Guaranteed Program
100+ Tied-up Companies
Unlimited Live Classes Access
100% Job Guarantee
1-on-1 Interview Mentorship
Thrice a Week Doubt Clarification Classes
8+ Real-Time Projects to Work & Place in Resume
Resume Roles & Responsibilities
Internship Certificate
Learn 15+Tools

Meet Your Trainer: Kanth

International Corporate Trainer with 10+ years of extensive experience delivering advanced training across Generative AI, Agentic AI, Machine Learning, and Deep Learning, empowering professionals to build and deploy real-world AI solutions.
Designed and delivered customised corporate training programs for Fortune 500 clients and global EdTech partners, covering Generative AI, Agentic AI, Machine Learning, Data Engineering, and Big Data ecosystems.
Conducted hands-on workshops for Virtusa, Merck, Walmart, Bank of America and Barclays, enabling teams to build robust Data Science, Generative AI, Deep Learning, AI, ML pipelines, adopt MLOps best practices, and deploy scalable models in regulated environments.
Led cloud migration and data modernisation bootcamps for Walmart and EY, upskilling teams in Azure Data Factory, Databricks, Snowflake, and Google Cloud Platform, resulting in improved project efficiency.
Trained Suzlon’s engineering teams on leveraging Big Data frameworks (Hadoop, Spark) for real-time analytics and operational excellence in renewable energy data streams.
Enabled EXL Services and The Math Company data professionals to master Data Science, Deep Learning, Advanced Data Analytics, BI tools (Power BI, Tableau), and SQL optimisation, enhancing client reporting capabilities.
Delivered Generative AI and LLM Orchestration sessions using LangChain, Prompt Engineering, and RAG pipelines using Amazon Bedrock empowering participants to build Agentic AI solutions for intelligent automation.
Served as a Lead Trainer for Purdue University PG Program, mentoring post-graduate learners in AI, Data Science, and Cloud Computing, bridging the gap between academic theory and industry practice.

Hiring Partners

Fill Out The Form, To Get Career Transition Strategy

Contact us

Our Best Selling Courses

Hike Guaranteed Programs! Designed for Working Professionals, Career Gap Learners & Freshers

Data Engineer Course with Career Transition

₹44,999.00

Add to cart

Zero to Job-Switch in 180 Days to Data Science & Gen AI

₹56,999.00

Add to cart

Zero to a Data Analytics Job in 90 Days

₹44,999.00

Add to cart

Experience Building Course on AI & Generative AI

₹59,999.00

Add to cart

Generative AI, LLMs & Agents Course for Beginners

Enroll Course

3 Steps for Successful Career Transition

We Will Help You Every Step Of The Way

01

Get Detailed understanding about Job-Ready "T" Skillset

02

Develop Confidence on Job-Ready Skillset by Implementing them on Real-Time Projects with BEPEC Internship

03

Market Your Skillset and Projects using right Roles & Responsibilities and Projects

What People Say About Us

Client Testimonials

I had a great experience with BEPEC Solutions during my career transition. Their team provided clear guidance and personalized support, making the process smooth and stress-free. The career coaches are knowledgeable and genuinely care about their clients' success. Thanks to their resume and interview prep, I landed a job that aligns with my goals. I highly recommend BEPEC Solutions to anyone looking for professional growth and a seamless career change.

Sharun

Hi, I feel grateful to be a student of BEPEC. This is genuine and I would say most budget friendly institution to go for any course! Talking about learnings, 5/5. KANT SIR's teaching and patience is just so good to deal with each student. You get the recordings as well. Thanks for the amazing coaching!!

Shaily Srivastava

I have enrolled for the Data Science Career Transition Program through Bepec Kanth explains how to apply all the concepts in real world. Along with developing domain knowledge he is also helping to enhance our professional skills to face real world challenges as data scientist... What ? Why? Where ?.....3 words most important

SREE SAI KONDLE

#1 Full Stack Data Engineering Course
for Working Professionals | 100% Job Guarantee

Career Transition Checklist

Data Engineer Key Skills from BEPEC Program

Full Stack Data Engineer Career Transition Program with Remote Internship

Data Engineer End-to-End Projects

Key Program Highlights

No-Coding to Job-Ready Data Engineering Course Curriculum

Why Choose BEPEC for Data Engineer Course

Data Engineer Career Transition Program

Data Engineer Career Transition Program