How to Become a Big Data Engineer: 8 Steps, Essential Skills, and Career Opportunities for 2025

Big data engineering is a rapidly growing field in India, fueled by the increasing volume of data generated across industries. With a CAGR of 36.7%, the sector is expected to reach USD 86.9 billion by 2027. 

A big data engineer at a top tech company works on optimizing data pipelines to help reduce costs or improve customer experiences.

This blog explores how to become a data engineer, highlighting the steps, essential skills required, and career opportunities for big data engineers in 2025.

How to Become a Big Data Engineer: 8 Steps to Success?

A data engineer is well-versed in some specific skill sets. This includes building a strong foundation in computer science, mastering key programming languages, and gaining hands-on experience with big data tools. Let’s first understand what the Big Data is:

What is Big Data?

Big data refers to datasets that are vast, complex, and grow at an exponential rate. These datasets often exceed the processing capabilities of traditional data management tools. Its defining characteristics are often summarized by the 5 Vs:

• Volume: The sheer size of data, such as petabytes of user data generated by social media platforms.
• Velocity: The speed at which data is generated, such as real-time stock price updates or live streaming analytics.
• Variety: The diverse formats and types of data, like IoT data from connected devices, images, and text-based logs.
• Veracity: Ensuring data accuracy and reliability, such as validating weather data collected from multiple sensors.
• Value: Extracting actionable insights from raw data, like analyzing customer purchase patterns to enhance product recommendations.

Also Read: Sources of Big Data: Where does it come from?

Ways to Become a Big Data Engineer in India

To start your journey as a Big Data Engineer, focus on building a strong foundation in key technical skills, programming, and data management principles. Here’s how you can begin:

1. Build a Strong Educational Foundation

A degree in computer science, IT, or mathematics forms the foundation for a big data engineering career. Certifications in tools like Hadoop or Spark, paired with hands-on projects, enhance expertise and employability.

• Bachelor’s Degree: Pursue a degree in computer science, engineering, or information technology. For example, programs like B.Tech in Computer Science or B.Sc. in IT provide foundational skills in programming and database management.
• Advanced Degrees: Consider a master’s degree in data engineering or analytics, such as an Masters in Data Science Degree (Online) or specialized certifications like upGrad’s certification course in big data, to gain a competitive edge.

As you establish a solid educational foundation, the next step is to focus on mastering the programming languages that will form the core of your big data engineering skills.

2. Learn Programming Languages

Programming languages form the core toolkit for big data engineers. Key languages to focus on:

• Python: Widely used for data manipulation, especially with libraries like Pandas and NumPy.
• Java: Often used with frameworks like Hadoop due to its reliability and performance.
• Scala: Essential for working with Apache Spark and other big data processing tools.

In addition, learning SQL for querying databases and Bash for scripting is highly beneficial. The rise of cloud-based platforms such as AWS, Google Cloud, and Microsoft Azure also means learning cloud-specific tools like AWS Lambda and Google BigQuery will be increasingly important.

Mastering programming languages sets the foundation for working with big data, but to truly excel, it’s crucial to understand the tools and technologies that bring your code to life in the big data ecosystem.

3. Understand Big Data Tools and Technologies

Familiarity with big data tools and technologies is essential for building and managing large-scale data systems effectively. These tools play a critical role in handling diverse tasks, from storage to real-time processing.

• Hadoop: A foundational tool for distributed storage and processing of massive datasets.
• Spark: Known for its real-time, in-memory data processing capabilities, making it ideal for quick computations.
• Kafka: Essential for managing real-time data streams, allowing efficient data movement between systems. Stream processing tools like Kafka are crucial for applications that require continuous data ingestion, such as real-time analytics or monitoring systems.
• Flink & Apache Beam: Emerging tools designed for advanced real-time stream processing, supporting complex event processing in dynamic environments.

Additionally, technologies like Delta Lake and Apache Iceberg are becoming vital for managing data lakes, offering capabilities like schema enforcement, ACID transactions, and optimized query performance. 

These tools bridge the gap between big data storage and real-time processing, ensuring seamless data management and reliability in modern big data systems.

Also Read: Big Data and Hadoop Difference: Key Roles, Benefits, and How They Work Together

As you deepen your understanding of big data tools, it’s equally important to strengthen your knowledge in database management, ensuring you can effectively store, retrieve, and process large datasets across various systems.

4. Gain Expertise in Database Management

Big data engineers must excel in managing both SQL and NoSQL databases to handle diverse data requirements:

• SQL Databases: Tools like MySQL and PostgreSQL remain essential for managing structured data, such as financial records or user profiles, where relationships between datasets are critical.
• NoSQL Databases: MongoDB, Cassandra, and HBase are ideal for unstructured or semi-structured data, such as social media content or IoT sensor data, where flexibility and scalability are key.
• Cloud-Based Databases: Services like Amazon Aurora and Google Cloud Spanner provide scalable, managed solutions that are particularly valuable for startups and growing businesses. For example, Amazon Aurora can automatically scale storage and processing as demand increases, making it perfect for applications with unpredictable workloads, like e-commerce platforms or SaaS tools.

Database security is essential, including encryption and GDPR compliance, as data volume and complexity increase.

Also Read: Top 10 Big Data Tools You Need to Know To Boost Your Data Skills in 2025

As you build your database management expertise, the next step is to focus on developing the essential data engineering skills that will allow you to design, implement, and optimize data systems effectively.

5. Develop Data Engineering Skills

A successful big data engineer requires a blend of technical expertise and industry best practices to design and maintain efficient data systems. These skills enable the seamless processing, storage, and management of large datasets critical to modern business operations.

• ETL Processes: Master the creation of efficient Extract, Transform, Load pipelines to move and prepare data for analysis.
• Data Storage Management: Understand batch and real-time processing systems to handle diverse data workflows, such as processing daily sales reports or real-time stock market feeds.
• Data Quality & Integrity: Ensure clean, reliable data throughout the pipeline, as poor-quality data can lead to inaccurate insights and costly business errors.
• Orchestration Tools: Tools like Apache NiFi and Airflow are essential for automating and managing complex workflows. For instance, Airflow can schedule and monitor daily pipeline tasks in e-commerce platforms, ensuring timely data delivery for inventory tracking.
• Industry Best Practices: Implement CI/CD pipelines to streamline the deployment of data systems and enhance scalability. Incorporate observability tools like Prometheus or Grafana to monitor data pipeline performance, identify bottlenecks, and maintain uptime.

Also Read: Top 6 Skills Required to Become a Successful Data Engineer

To bridge the gap between building foundational skills and gaining practical experience, applying your knowledge through real-world projects is the perfect next step.

6. Work on Real-World Projects

Practical experience is crucial to mastering big data engineering. While theoretical knowledge forms the foundation, applying that knowledge to solve real-world problems is what sets you apart. 

Real-world projects not only help you understand how tools and technologies work in practice but also enhance your portfolio, showcasing your capabilities to potential employers.

Here’s how to gain hands-on expertise:

• Contribute to Open-Source Projects: Platforms like GitHub offer collaborative opportunities in data engineering. For instance, search for projects tagged with "big data" or "Hadoop," where you can contribute to tasks like building ETL pipelines or optimizing distributed processing.
• Internships: Work with leading companies like Zomato or Swiggy to gain exposure to large-scale data systems and real-time analytics in action.
• Freelance Projects: Take on freelance assignments or collaborate on industry-specific challenges, such as optimizing data pipelines for e-commerce or processing IoT sensor data for smart devices.
• Cloud Platforms and Stream Processing: Engage in projects involving tools like Kafka or Flink to build real-time data processing systems, especially on cloud platforms like AWS or Azure.

As you gain hands-on experience through real-world projects, earning certifications and staying updated will further solidify your expertise and keep you ahead in the fast-evolving field of big data.

7. Get Certified and Stay Updated

Certifications not only validate your expertise but also help you overshadow competition. They demonstrate your commitment to learning and adapting to new technologies, making you more appealing to employers.

Consider pursuing certifications like:

• upGrad’s Executive PG Programme in Data Science
• upGrad’s Executive Post-Graduate Programme in Business Analytics

Staying updated is equally important. Regularly explore webinars, take advanced courses, and attend industry conferences to learn about the latest tools and emerging trends in big data engineering. This ensures your skills remain relevant and aligned with industry demands.

As you gain new skills and certifications, connecting with industry professionals can further enhance your learning and open up exciting career opportunities.

8. Network with Industry Professionals

A strong professional network can significantly accelerate your career in big data. It connects you with industry leaders, exposes you to the latest trends, and opens doors to collaborations and job opportunities. Networking also provides access to mentorship, helping you navigate challenges and refine your skills.

Here’s how you can expand your network:

• LinkedIn: Connect with big data engineers worldwide, participate in relevant groups, and share insights to showcase your expertise.
• Industry Events: Attend conferences like Big Data LDN (London), Strata Data Conference (U.S.), or O'Reilly Data Science Conference (Global) to engage with thought leaders and learn about emerging technologies.
• Meetups & Webinars: Join local or virtual big data meetups and webinars to gain insights from experienced professionals and discuss practical challenges.

Building connections with industry professionals can provide valuable insights into the skills and trends shaping the future of big data engineering. With the right network, you can sharpen the essential skills needed to thrive in the industry..

Must-Have Skills for Big Data Engineers

To excel as a big data engineer, develop the following skills:

• Programming Proficiency: Python, Java, Scala.
• Big Data Tools: Hadoop, Spark, Kafka.
• Database Management: SQL, NoSQL.
• Cloud Computing: AWS, Azure, Google Cloud.
• Data Visualization: Tableau, Power BI.
• Problem-Solving and Analytical Thinking: To address real-world challenges.
• Version Control: GitHub for collaboration.
• Soft Skills: Communication, Teamwork, Presentation Skills

Also Read: 8 Best Big Data Courses For Graduates To Elevate Your Career

As the demand for big data engineers continues to rise, it’s essential to not only master key skills but also understand the career opportunities that await in 2025. 

Best Career Opportunities for Big Data Engineers in 2025

The demand for big data engineers in India is booming. Industries like finance, healthcare, and e-commerce offer lucrative opportunities. Here are some of the top roles with average annual salary packages:

Source: Glassdoor

Also Read: Top 12 In-Demand Big Data Skills To Get ‘Big’ Data Jobs in 2025

The demand for big data engineers is making it an exciting career opportunity. Let’s see how upGrad can help you build the skills and confidence to thrive in the industry.

How upGrad Can Help You Build a Career as a Big Data Engineer?

Becoming a data engineer begins with learning core skills like database management, and programming languages such as Python or SQL, tools like Apache Spark and cloud technologies, such as AWS or Azure. This role offers opportunities to work on complex data systems of businesses helping them to make key business decisions.

upGrad offers specialized programs to learn Big Data and Data Analysis, combining in-depth technical training, hands-on projects, and real-world case studies. 

Here are some courses that can help you achieve your career goals:

• Analyzing Patterns in Data and Storytelling
• Data Science in E-Commerce
• Linear Algebra for Analysis
• Basics of Inferential Statistics
• Case Study Using Tableau, Python, and SQL
• Professional Certificate Program in Business Analytics & Consulting
• Post Graduate Certificate in Data Science & AI (Executive)
• Professional Certificate Program in AI and Data Science

Do you need help deciding which courses can help you excel in big data? Contact upGrad for personalized counseling and valuable insights. For more details, you can also visit your nearest upGrad offline center.

Unlock the power of data with our popular Data Science courses, designed to make you proficient in analytics, machine learning, and big data!

Explore our Popular Data Science Courses

Elevate your career by learning essential Data Science skills such as statistical modeling, big data processing, predictive analytics, and SQL!

Top Data Science Skills to Learn

Stay informed and inspired  with our popular Data Science articles, offering expert insights, trends, and practical tips for aspiring data professionals!

Read our popular Data Science Articles