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IntermediateDatabase & Data

Data Engineer Roadmap

Build scalable data pipelines and infrastructure

Duration
10-12 months
Job Demand
very high
Phases
6
Views
2029

About This Roadmap

Data Engineering is the backbone of modern data-driven organizations, focusing on building and maintaining the infrastructure that enables data collection, storage, and processing at scale. As a data engineer, you will design and implement data pipelines, manage databases, work with big data technologies, and ensure data quality and availability. This comprehensive roadmap covers SQL and NoSQL databases, ETL/ELT processes, data warehousing, big data tools like Spark and Kafka, cloud platforms, and orchestration tools like Airflow. You will learn to handle petabytes of data, optimize query performance, and build real-time data streaming systems. Data engineers are in extremely high demand as companies increasingly rely on data for decision-making. The role requires strong programming skills, understanding of distributed systems, database expertise, and knowledge of cloud infrastructure.

Prerequisites

  • Programming knowledge (Python/Java)
  • SQL basics
  • Understanding of databases
  • Linux fundamentals

What You'll Learn

Build data pipelines
Master SQL and NoSQL
Work with big data
Deploy on cloud
Implement ETL/ELT
Optimize data systems
Ensure data quality

Complete Learning Path

8-10 weeks

1
Python Advanced

OOP, error handling, file I/O, libraries

2
SQL Mastery

Complex queries, optimization, indexing, transactions

3
PostgreSQL

Administration, performance tuning, replication

4
NoSQL

MongoDB, Redis, Cassandra, use cases

6-8 weeks

1
Relational Modeling

Normalization, ER diagrams, foreign keys

2
Dimensional Modeling

Star schema, snowflake schema, fact tables

3
Data Warehousing

OLAP vs OLTP, data marts, slowly changing dimensions

4
Data Lakes

Architecture, storage formats (Parquet, Avro)

8-10 weeks

1
ETL Fundamentals

Extract, transform, load processes

2
Apache Airflow

DAGs, operators, scheduling, monitoring

3
Data Quality

Validation, testing, monitoring

4
Version Control

Git, CI/CD for data pipelines

10-12 weeks

1
Apache Spark

RDDs, DataFrames, PySpark, optimization

2
Hadoop Ecosystem

HDFS, MapReduce, Hive, HBase

3
Stream Processing

Apache Kafka, real-time pipelines

4
Data Formats

Parquet, ORC, Avro, compression

8-10 weeks

1
AWS

S3, Redshift, Glue, EMR, Lambda

2
GCP

BigQuery, Dataflow, Pub/Sub, Cloud Storage

3
Azure

Synapse, Data Factory, Databricks

4
Infrastructure as Code

Terraform, CloudFormation

6-8 weeks

1
Data Governance

Security, compliance, lineage, cataloging

2
Performance Optimization

Query tuning, partitioning, caching

3
Real-time Analytics

Lambda architecture, Kappa architecture

4
DataOps

Automation, monitoring, observability

Tools & Technologies

PythonSQLPostgreSQLMongoDBSparkKafkaAirflowDockerAWSGCPAzureTerraformHadoop

Career Opportunities

  • Data Engineer
  • Big Data Engineer
  • ETL Developer
  • Data Platform Engineer
  • Analytics Engineer
  • Data Architect

Expected Salary (India)

₹6-12 LPA (Entry)
₹12-25 LPA (Mid)
₹25-45 LPA (Senior)
₹45L+ (Lead)

Companies Hiring

GoogleAmazonMicrosoftMetaNetflixUberAirbnbFlipkartSwiggyPaytmRazorpayZerodha

Ready to Start?

Follow this roadmap step-by-step and track your progress.

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