Roadmap for Data Engineering 2025
Table of Contents
- jaro Education
- 2, November 2024
- 12:00 pm
Data is increasing rapidly; hence, skilled experts are needed to make the most of it. Because Zippia predicts a huge surge of 21% in data professional jobs from 2018 to 2028, there is currently more demand for skilled data engineers than ever. Data engineers help create and support the needed infrastructure to convert raw data into useful information.
The data engineering roadmap for 2025 provides an overview of the sequential steps to take while mastering basic skills such as data modelling, ETL, big data, and cloud engineering. Thus, whether you are a newbie or an IT specialist wishing to advance your career, this roadmap provides the necessary tools, programming languages, certifications, and best practices that will ensure your success in this area.
What Does a Data Engineer Do?
The work and input of data engineers in the entire data ecosystem are significant since they are responsible for creating, implementing, and managing the entire infrastructure, enabling any institution or individual to efficiently aggregate, process, and analyse vast amounts of information.
The main focus of the big data engineer is to make sure that the data provided to data scientists or business analysts is readily available, trustworthy, and prepared for analysis. It entails the construction of data pipelines for the purpose of transporting relevant data automatically from respective sources into databases or data warehouses.
Future of Data Engineering
Maxime Beauchemin was the first data engineer at Facebook and Airbnb, creating and sharing Apache Airflow, an orchestrator that’s used widely by many, and soon followed by Apache Superset, a data exploration tool that’s quickly becoming popular in the data viz world. Currently, Maxime leads and co-owns Preset, a quickly expanding start-up that’s helping companies use AI for data visualization.
It’s accurate to note that Maxime played a major role in building and creating some of the most impactful data engineering Roadmap 2025 tools of the last decade and shared this experience widely in his blog post, The Rise of the Big Data Engineer Roadmap. Essentially, according to Maxime, for data science and analytics to grow, data teams require an experienced big data engineer Roadmap to manage ETL, build pipelines, and expand their infrastructure.
Based on this picture, the data engineer’s role becomes clear. Building and optimizing how data arrives, stores and is used for analysis are key tasks for the data engineer as a member of the data team.
Steps to Become a Data Engineer: Roadmap
This roadmap outlines the key stages and technologies one must follow to succeed in data engineering. Data engineering roadmap 2025 is a field that involves a lot of learning, and therefore requires being inquisitive and practicing to get the hang of using their tools and techniques. Here are the basic steps you should go through to become a big data engineer: Roadmap.
1. Step 1: Foundational Skills Building
It is best to start your big data engineer Roadmap adventure firmly grounded. Students in this section gain experience with programming, study the basics of computer science, and learn how to connect with databases via SQL.
We start with Python Programming
You need to be familiar with Python’s syntax, data processes, controls and functions to make the most of your data. Because of the wide range of uses Python supports, it is valued in big data engineer Roadmap.
An Introduction to Computing
Become familiar with main computer science topics, including memory, types of algorithms and data growing trends. You learn how to handle and preserve data inside your computer through the foundation.
What do we call this?
Understand that SQL is used to ask questions of relational databases. If you want to get, change or limit the data in a database, you should know SELECT, JOIN and WHERE.
- Nearly half the people surveyed in the 2023 Stack Overflow Annual Developer Survey rely on SQL and Python for programming.
- Readers will learn big Data Engineer Roadmap fundamentals with The CRCTC.
Resources:
- You need to have a good amount of knowledge for this part of the process. Use the resources given to help you as you learn.
- You can learn Python, computer science basics and SQL by using Scaler as a resource.
- How about reading through “Automate the Boring Stuff with Python” by Al Sweigart, “Python Crash Course” by Eric Matthes, “Introduction to Algorithms” by Cormen et al. and “Cracking the Coding Interview” by Gayle Laakmann McDowell?
- To help yourself write SQL queries, use the W3Schools SQL Tutorial and SQLBolt.
2. Step 2: Exploring Different Types of Databases
Understanding database types is very important as you get deeper into the Big Data Engineer Roadmap. At this step, you get the skills to select the proper storage for your various data types.
The relational databases MySQL and PostgreSQL are also included.
They keep information in an organized form by arranging data in table rows and columns. Get familiar with how schemas, normalization, and querying are done in relational databases.
Samples will also be using NoSQL Databases (MongoDB, Cassandra).
NoSQL enables organizations to handle unstructured or abundant amounts of data. Check out NoSQL databases including wiki-style, key-value pairs, graphs, and column formats and see what they are good for.
Storing data using Amazon Redshift and Google BigQuery is industry common.
A data warehouse is built to keep historical information saved in an organized way for detailed analysis. Review concepts in data warehousing (such as ETL and data modeling) and discover cloud-based services designed for use with data warehousing.
Resources:
In this step, you compare numerous database technologies. Check out these applications and websites to help you learn more:
- You can study relational databases, NoSQL databases and data warehousing using online courses provided by jaro education
- Next, we’ll go to Step 3 and start exploring the area of data processing.
3. Step 3: Mastering Data Processing
The main part of big data engineer Roadmap involves turning data into a useful form. It helps you understand how to get and get ready data for study.
ETL (Extract, Transform, Load) is a basic ETL process.
Moving data from different sources to a data warehouse or lake is possible thanks to the ETL process. Learn about extraction, transformation and loading in ETL and look into the tools used for bringing data together.
Resources:
- At this phase, the main topic is data processing techniques. Here you will find some helpful resources for your study:
- Check out online sites that offer classes on data loading, batch processing and streaming processing.
- Data Engineering Roadmap 2025: Building Scalable Analytics Systems” by Matt Leahu and Streaming Data Processing with Apache Flink” by Fabian Hueske, are books you might want to study to gain more knowledge.
4. Step 4: Cloud Technologies Exploration
Cloud computing lets you make use of computing power, storage space, and databases whenever you need them. Learn about fundamental services such as compute, storage, and databases, provided by major cloud suppliers Amazon Web Services (AWS) and Google Cloud Platform (GCP). Learn how these platforms are used in the data engineering roadmap 2025
Work with the Cloud: Many suppliers let you use their services for free or with trial accounts to try them out.
Resources:
Here, data engineers explore how to use cloud computing for their operations. These resources will help you begin your study:
- Look at AWS’s and Google Cloud’s guides to better learn about their platforms.
- Online Learning: Use websites that let you study cloud computing basics and specific classes on Amazon Web Services (AWS) and Google Cloud Platform (GCP).
5. Step 5: Learning Big Data Technologies
Hadoop Ecosystem:
Hadoop makes it possible to handle and store vast volumes of data on multiple computers. Pay attention to the major pieces of Hadoop such as HDFS, YARN and MapReduce. Understanding Hadoop ecosystem helps you make use of a widely popular big data processing tool.
Apache Spark:
A lot of people rely on Spark to manage their data processing needs when using Hadoop. Take time to understand Spark’s parts (for example, Spark SQL, Spark Streaming) and why it runs more efficiently using in-memory processing, setting it apart from MapReduce.
Resources:
Here, I focus on the building blocks of big data. There are some important tools here to support your progress:
- Courses: You can find courses online about both the Hadoop ecosystem and Apache Spark.
- For a comprehensive look into Hadoop, read “Hadoop: The Definitive Guide” by Tom White. “Learning Spark” by Holden Karau et al. is a good way to start learning Spark.
6. Step 6: Building Data Pipeline Skills
You are ready to start building data pipelines after you have built the necessary data engineering technologies. At this point, you develop the skills needed to build and manage workflows that guide your data from where it originates to where you need it.
Learning to build and use a data pipeline is important
Make sure you know how to move data from several sources, make it ready for analysis and move it to locations where analysts can do that work. Examine the main tools used for scheduling data operations such as Apache Airflow, Luigi and Prefect.
Working with data yourself is the best method for improving your data pipeline skills. You can also try personal projects or add to open-source projects to improve your portfolio and reveal your abilities to others.
Resources:
Step 5 is devoted to assembling and developing data pipelines. The following are some sites to help you learn more effectively:
- You should look into Internet-based courses focused on developing data pipelines and using widely used orchestration technologies.
- You can learn the details of data pipelines through Scaler’s Data Science Course. Follow the guidance of trained specialists and pick up work skills.
- Documents and instructions are available in tutorials and program manuals for tools such as Apache Airflow, Luigi and Prefect.
7. Step 7: Gaining Practical Experience
With the knowledge of data engineering roadmap 2025 concepts and techniques in hand, you should now gain practical skills by doing tasks. At this point, project suggestions are grouped by challenge to allow you to work on your skills and build a powerful portfolio.
Freshers should start with Beginner Projects that take 1-2 Months.
For practice, write a Python scraper that takes data from a webpage (product information, statistics or weather numbers, etc.). You can choose to use Beautiful Soup or Scrapy libraries.
Find government or open data online and try to clean it using Pandas and NumPy Python libraries to handle any issues or formatting problems and include methods for transforming and processing the data.
Making a Basic Data Pipeline: Make a basic data pipeline with Apache Airflow or any other tool. You might gather the data, make simple changes to it and insert it into SQLite.
Intermediate Projects take between 2 and 4 months to complete.
Explore how to process real-time data by running simple temperature inputs and setting up a streaming pipeline using Apache Spark Streaming in your context. Use Apache Kafka as a dashboarding tool to see your data in real time.
To build a Recormataudation Engine, use collabfartika infekation techniques on a movie database or a database of your preference. You can find libraries such as scikit-learn that make it simple to put recommendation algorithms into practice.
You can use an online cloud service from AWS or Google Cloud to establish your data warehouse. Obtain data from a variety of places, process it and send it into the cloud data warehouse for analysis.
The full duration for advanced projects is four months or more.
Total Your Data Engineering Skills with Machine Learning by Creating a Complete Pipeline for Machine Learning Projects. Here, one might do data cleaning, work on making useful features, train a model and assess it with TensorFlow or PyTorch.
Design an analytics dashboard to visualize information that arrives continuously (from any streaming source such as social media or the stock market). Stream the data into Apache Kafka and use Apache Flink to handle it. After that, you can show the information visually in interactive dashboards made with Plotly or Dash.
Handle and explore a huge amount of data using Apache Spark which is suitable for working on numerous machines. Perform evaluations on huge collections, spot abnormal data or identify the mood of information.
Key Responsibilities of a Data Engineer
- Data Collection and Integration: Engineers’ data collection efforts involve data extraction from multiple sources, APIs, databases, external providers, and so forth. This helps in building up efficient data storage pipelines for the data to be ingested into database systems.
- Data Storage and Management: Data storage management is the subsequent stage where data engineers must develop ways to store and manage the collected data. This involves choosing the right database systems (SQL or NoSQL), modifying data structures for the best performance, and maintaining the consistency of the data.
- Building Data Pipelines: In the process of the data engineering roadmap 2025, a great extent of work goes into building solid processes for ETL (Extract, Transform, Load). Such pipelines take the unstructured data and prepare it for analysis with quality assurance at every level.
- Collaboration with Data Teams: Attending to the analytical needs of the data scientists and analysts, data engineers make sure that infrastructure improves their analytics. In such a case, they assist in developing an effective system whereby optimised value is obtained from the raw data available.
- Monitoring and Maintenance: Data engineers deploy data systems and then monitor and evaluate them on dispensing tasks for dependability and effectiveness. If a problem arises, they fix it and look for ways to enhance performance.
- Data Security: Finally, the data storage problem for any sensitive information in any system has to be solved on time. Data protection mechanisms are put in place by data engineers to limit the access of this information to authorised personnel only.
Data Engineer Skills Required
To be a proficient data engineer, one has to acquire some higher-level skills. Some of the Data Engineer Roadmap skills are discussed below:
- Cloud Computing: Knowledge of working on cloud platforms, such as AWS, Google Cloud Platform (GCP), Microsoft Azure, etc., is very important because most companies have started to shift their infrastructure onto the cloud.
- Big Data Technologies: Tools such as Apache Spark, Hadoop, Hive, and Kafka are used for processing large quantities of data.
- Data Warehousing Solutions: It would greatly help your skills if you knew how to architect and operate data warehouses inside Amazon Redshift or Google BigQuery.
- Data Modeling: It is also important to know how to develop data models that meet the needs of businesses and, at the same time, perform effectively.
- Machine Learning Basics: Not always, but it is helpful to have at least the basics of machine learning when working with data scientists.
Salary Expectations
The data engineer’s salary varies based on factors such as experience level, location, and industry:
Data Engineer | Work Experience | Salary in INR per Year |
0 to 1 year | ₹3.5 – ₹6.5 LPA | |
1 to 3 years | ₹6 – ₹10 LPA | |
4 to 6 years | ₹10 – ₹18 LPA | |
7 to 9 years | ₹18 – ₹28 LPA | |
10 to 14 years | ₹28 – ₹45 LPA | |
15+ years | ₹45 LPA and above |
Source: Glassdoor
Top Tools for Data Engineers in 2025
Data engineers use a range of tools designed for particular purposes. Mastering all tools is not a requirement, but one should know the basic principles of some core technologies. Below are examples of data engineering tools:
Databases
SQL is one of the fundamental tools for data engineers. Relational database management systems are based on a structured approach whereby data is organised in tables, and the language used for this is referred to as structured query language. MySQL, PostgreSQL, and Oracle are examples of popular SQL databases.
In contrast, NoSQL databases employ a non-relational approach where data is not restricted to being stored in tables. Several applications are designed for handling this kind of data. For instance, MongoDB, Cassandra, and Redis are examples of applications developed for unstructured data storage and processing.
Data Processing
Looking at contemporary businesses, the need for speed has made organisations embrace real-time data processing. This has led data engineers to build streaming pipelines that allow the processing of data to be instant. Apache Spark is extensively used when analytics and processing of data are required.
Programming Languages
In a way, programming is the solution to any data challenge. Python has become the most preferred programming language for data engineers and other stakeholders in data science analytics due to its simplicity of learning, ease of syntax, and vast libraries that can be used for data-related projects.
Data Migration and Integration
Data migration refers to transporting data while retaining its quality and meaning, and data integration involves data from several systems into a single one to aid in its analysis. Striim is a popular real-time application that provides data migration as well as integration within and between the public and private cloud infrastructure.
Distributed Systems
To cope with very large amounts of data, there is a need for distributed systems that allow computation and storage of data across several nodes. Hadoop is one of the most widely used platforms that allows data management through a distributed computing environment.
Data Science and Machine Learning Tools
Data science tools are not a must-have for data engineers, but having an understanding of such core tools enables them to work well with data scientists. Two of the most used open-source machine learning libraries used for model development and deployment are PyTorch and TensorFlow allowing users to perform deep learning using CPUs and GPUs where applicable.
Boost Your Career with a Certificate Course by CEP, IIT Delhi
The Executive Programme in Applied Data Science using Machine Learning & Artificial Intelligence Programme offered by CEP, IIT Delhi, is designed for professionals who aspire to integrate the data science capabilities of the organisation to the next level. Graduate participants acquire advanced analytical skills, machine learning and data visualisation. The programme concentrates on the application area and provides participants with a means of working with data and making decisions based on it.
Eligibility Criteria
- Educational Qualification: Graduate or Diploma holder (10+2+3) in Science, Engineering, Mathematics, or Statistics.
- Work Experience: Minimum of 1 year of relevant professional experience.
Jaro Education Career Counselling – Your First Step to Success
Jaro Education has consistently empowered thousands of learners to achieve their professional goals through expert career counselling and industry-aligned programs. With a proven track record of over 3 lakh professionals successfully upskilled and placed in top companies, Jaro’s guidance has helped individuals choose the right career path, enhance their qualifications, and secure higher-paying roles. The success rate of Jaro Education lies in its personalized approach, strong academic collaborations, and focus on real-world outcomes, making it a trusted partner in career transformation.
Final Takeaways
Data engineering is a useful subsection of data science. It includes the anthropogenic processes necessary for constructing systems that process large amounts of data. Having a vision for how to acquire and perfect one’s data engineering competencies with time, through education, work, or even the right level of IT professionalism. Moreover, data engineering, which comes with a good salary and more opportunities in the current era, remains relevant in all sectors of the economy.
Frequently Asked Questions
Trending Blogs
