Big Data Engineer

Big data is a field that treats ways to analyze, systematically extract information from, or otherwise deal with data sets that are too large or complex to be dealt with by traditional data-processing application software.

Big Data Engineer

1. What It Is

A Big Data Engineer designs, develops, and maintains systems for collecting, storing, processing, and analyzing massive datasets. They build data pipelines, develop data warehouses, and ensure data quality, reliability, and security. This differs from a typical software engineer because of the scale and complexity of data involved and the specialized tools needed.

2. Where It Fits in the Ecosystem

Big Data Engineering resides within the data engineering and data science ecosystem. It is the foundation for data analysis, machine learning, and business intelligence. Big Data Engineers work closely with data scientists, data analysts, and business stakeholders.

3. What to Learn Before This

• Basic Computer & Internet Knowledge
• Programming Fundamentals (Python, Java, or Scala)
• Data Structures and Algorithms
• SQL and Relational Databases
• Linux Command Line Basics
• Git & GitHub (Version control)

4. What to Learn After This

• Hadoop Ecosystem: HDFS, MapReduce, YARN
• Spark (for data processing and analytics)
• NoSQL Databases: Cassandra, MongoDB, HBase
• Cloud Platforms: AWS, Azure, GCP (related big data services)
• Data Warehousing: ETL processes, data modeling
• Data Streaming: Kafka, Flink
• Data Governance and Security
• Data Pipeline Technologies: Airflow, Luigi
• Containerization: Docker, Kubernetes

5. Similar Roles

• Data Engineer - Broader than Big Data Engineer, may include smaller datasets
• Data Architect
• Data Scientist - Focuses on analysis and modeling, not infrastructure
• ETL Developer
• Cloud Data Engineer

6. Companies Hiring This Role

• Tech Companies (Google, Amazon, Microsoft, Facebook)
• E-commerce Companies (Walmart, Amazon, eBay)
• Financial Institutions (JPMorgan Chase, Bank of America)
• Consulting Firms (Accenture, Deloitte, McKinsey)
• Healthcare Providers
• Startups dealing with large datasets

7. Salary (as of 2025)

• India

  • Freshers: ₹4-8 LPA
  • Mid-level (3-5 yrs): ₹10-25 LPA
  • Senior: ₹20-45+ LPA

• US

  • Entry-level: $80K-$120K/year
  • Mid-level: $120K-$160K/year
  • Senior: $160K-$220K+/year

8. Resources to Learn

Free

• Apache Hadoop Documentation
• Apache Spark Documentation
• Cloud provider documentation (AWS, Azure, GCP)
• YouTube Tutorials (DataCamp, freeCodeCamp)

Paid

• Databricks Academy
• Udemy - "The Complete Apache Kafka Course"
• Coursera - "Data Engineering Specialization"
• Udacity - "Data Engineering Nanodegree"

Books

• "Hadoop: The Definitive Guide"
• "Spark: The Definitive Guide"
• "Designing Data-Intensive Applications"

9. Certifications

• AWS Certified Big Data - Specialty
• Google Cloud Certified Professional Data Engineer
• Azure Data Engineer Associate
• Cloudera Certified Data Engineer

10. Job Outlook & Future

• High Demand: Data volumes are constantly growing.
• Cloud Adoption: More companies are moving big data infrastructure to the cloud.
• Data Governance: Increasing focus on data quality and security.
• Automation: More tools are emerging to automate data pipeline development.

11. Roadmap to Excel (Simple English)

Beginner

1. Learn Python or Java programming.
2. Understand SQL and relational databases.
3. Learn Linux command-line basics.
4. Set up a Hadoop cluster (virtual or cloud-based).

Intermediate

5. Learn Spark for data processing.
6. Explore NoSQL databases like Cassandra or MongoDB.
7. Build ETL pipelines for data ingestion and transformation.
8. Learn data streaming with Kafka.

Advanced

9. Design and implement data warehouses.
10. Master cloud-based big data services (AWS, Azure, GCP).
11. Implement data governance and security policies.
12. Automate data pipeline deployment with Docker and Kubernetes.
13. Contribute to open-source big data projects.