What is a Data Engineer? 9 Steps to Become One in 2025

What Is a Data Engineer?

A data engineer is someone responsible for designing, building, and maintaining the architecture and systems that allow organisations to collect, store, process, and access data at a varied scale. They help the machine learning and analytics team after dealing with the raw data.

This differs from a data analyst, who interprets data, or a data scientist, who builds models and creates insights; a data engineer focuses on building the systems that make analytics possible.

For example, while a data scientist might ask “what patterns exist in customer behaviour?”, the data engineer makes sure the customer behaviour data is clean, available, and in the right form for the scientist to use.

To put it in context, imagine a company like Netflix or Flipkart. They deal with large volumes of streaming data: user clicks, watch times, and inventory movement. The data engineer sets up data pipelines to ingest this data, transforms it (e.g., aggregations, cleaning), stores it in warehouses, and ensures the analytics teams can query it efficiently. Without data engineers, the insights wouldn’t happen.

Thus, when you are exploring your career, understanding the data engineer roadmap, skills required, and what differentiates the role is a must.

What Does a Data Engineer Do?

Data engineers carry out a variety of responsibilities, often focused on enabling data-driven decision-making by ensuring that data is accessible and reliable. Some of the major tasks include:

• Building and maintaining data pipelines: This includes extract-transform-load (ETL) or extract-load-transform (ELT) tasks, moving data from source systems into data warehouses or data lakes, sometimes in real time.

• Data modelling and warehousing: Structuring data for analytics by designing schemas, data marts, star-schemas, dimension/fact tables, ensuring the data is optimised for queries.

• Cloud infrastructure & big data: Setting up systems on cloud platforms (AWS, GCP, Azure), managing distributed processing frameworks (e.g., Hadoop, Spark), handling NoSQL databases, etc.

• Real-time data processing: For many businesses, data needs to be ingested and processed in real time (streaming) rather than batch. Technologies like Kafka, Flink help here.

• Optimisation & performance tuning: Ensuring queries run fast, pipelines are efficient, data is fresh and reliable, monitoring for failure, and ensuring data quality.

• Collaboration: Working closely with data scientists, data analysts, ML engineers, and business stakeholders, understanding what data they need, how they will use it, and delivering products accordingly.

On a daily basis, a data engineer might be working on tasks like writing SQL queries to pull data, building a Spark job to process logs, setting up a scheduler (Apache Airflow) to orchestrate jobs, tweaking a data warehouse schema, troubleshooting pipeline failures, or discussing data access needs with analysts. Their role is both deep technical and highly cross-functional.

Why Choose a Career in Data Engineering?

Data engineering might feel a little challenging to pursue, but if you are considering this role in 2025, thence surely keep these factors in mind:

Rising Demand Across Industries

Almost every industry now relies on data: finance, healthcare, e-commerce, social media, and manufacturing, and as companies adopt more advanced analytics and AI, the infrastructure side becomes critical. Thus, there’s a growing demand for data engineers.

High Salary Potential

If you’re wondering about “data engineer salary”, the news is quite promising. Looking at recent salary ranges, in India, entry-level data engineers can expect around ₹5-10 Lakh per annum, mid-level can go ₹8-17 Lakh, seniors can hit ₹20 Lakh+ or much more depending on the company and skills. For global roles, especially in the US, salaries commonly reach $85K-202K+ for mid-level. So if you’re thinking about achieving growth potential with rewarding pay, then this career path certainly delivers.

Career Growth Opportunities

Data engineering is not a stagnant role; it offers a clear data engineer career path: you can start as a junior/associate DE, then move to senior, lead engineer, then architect, and eventually, roles like Chief Data Officer (CDO) are within reach. Because you’re at the core of the data infrastructure, you gain visibility, influence, and options.

Data Engineer Salary Insights

Here’s a breakdown of the data engineer salary ranges: Table: Salary Breakdown (India vs US vs Global)

Sources: Glassdoor Reports ,Indeed Insights

Key factors that influence salary: years of experience, location, e.g., tech hubs like Bengaluru vs smaller cities, or US West Coast vs other regions, specific skills like cloud, streaming, big data, company size, and industry, e-commerce, and fintech tend to pay more.

Engineer Career Path

The data engineer career path offers a structured journey filled with learning, growth, and changing responsibilities. Starting from entry-level roles focused on building and maintaining data pipelines, professionals can progress to senior and leadership positions where they design large-scale data architectures and lead teams. What makes this path exciting is how each stage consists of both technical expertise and strategic understanding of data systems. Whether you aim to become a senior data engineer, architect, or even a Chief Data Officer, this career path provides clear milestones, continuous skill development, and opportunities to shape how organizations handle the power of data.

Let’s walk through the data engineer career path, stage by stage:

At each stage, the data engineer's skills and responsibilities keep increasing. The difference between roles like a data engineer and a data scientist starts to blur at higher levels (e.g., architects may understand modelling, analytics, governance), but the key differentiation remains: engineers build the systems; scientists use them.

How to Become a Data Engineer (Roadmap)

Becoming a data engineer may sound complex, but with the right roadmap, it’s an achievable and rewarding journey. The role demands programming knowledge, database expertise, and cloud computing skills, but you don’t need to master everything at once.

By following a structured path that starts with foundational education, builds up through hands-on experience, and works in real-world projects and certifications, you can develop into a skilled data engineer. Whether you’re a student, a software developer, or someone switching careers, this step-by-step roadmap will guide you through the essential tools, technologies, and milestones needed to land your first data engineering role.

Let’s get into the data engineer roadmap, a step-by-step guide with an approximate timeline and tools to help you plan your path towards becoming a data engineer.

Step 1: Build a Strong Educational Foundation

Indeed, you don’t always need a degree, but most data engineers come from a Computer Science, IT, or software engineering background. It helps to have strong fundamentals: algorithms, data structures, databases, mathematics, and statistics.

Even if you have a non-CS degree, you can fill gaps via online courses. Certifications and bootcamps help too. For example, you may start learning SQL, basic programming, and then move into data-engineering-specific content.

You can check out Scaler’s Free Tutorials with Certificationfor starting!

Step 2: Learn Programming for Data Engineering

Key languages you must look into:

• Python: Very widely used for data engineering tasks, scripting, and automation.
• Java / Scala: Especially relevant if you work with big data platforms (e.g., Apache Spark).
• SQL: Vital for querying relational databases and data warehouses.

By this stage, you should get comfortable writing scripts, functions, using libraries (e.g., Pandas in Python), and understanding how code interacts with data systems.

Step 3: Learn Databases & Data Warehousing

This step is quite important; you should cover:

• Relational databases (SQL): PostgreSQL, MySQL, Oracle, learn query optimisation, indexing, joins, partitions. NoSQL / unstructured stores: MongoDB, Cassandra, and understand when to use them.

• Data warehouses/data lakes: Tools like Google BigQuery, Amazon Redshift, Snowflake help understand how large volumes of data are stored, accessed, and analysed.

This specialization gives you the pragmatics of how data is stored and used in businesses.

Step 4: Understand ETL & Data Processing

You must learn how data is moved and processed:

• Batch processing: Platforms like Hadoop, Hive, Spark, and batch jobs.
• Streaming / real-time processing: Kafka, Apache Flink, Spark Streaming. = ETL/ELT concepts: extraction from sources, transformation logic, and loading into target systems.

Step 5: Practice with Cloud Platforms

Modern data engineering is heavily cloud-centric:

• Learn AWS (e.g., S3, Redshift, Glue), Azure (Synapse, Data Factory), GCP (BigQuery, Dataflow).
• Aim for certifications like AWS Certified Data Analytics - Specialty, Google Professional Data Engineer; they help validate your skills. Working in cloud environments exposes you to scale, distributed systems, cost optimisation, security, and everything a true data engineer must handle.

Step 6: Learn Data Pipeline Orchestration

Building pipelines is one thing; scheduling and orchestration are also what you should be able to do:

• Use Apache Airflow, Luigi, or Prefect to build workflows, handle dependencies, monitor job runs, deal with failures, and re-runs. This maturity level is often what mid-level and senior data engineers have in their toolkit.

Step 7: Build Projects & Portfolio

Now it’s time to put your skills into practice:

• Create a sample sales analytics pipeline: ingest raw sales logs, clean them, store them in a warehouse, and build queries for insights.
• Build a real-time stock-data processor: use a streaming source, process events, store results, maybe alert on anomalies. Having concrete projects shows you are ready to apply the skills; it’s especially useful for interviews and for your first job.

Step 8: Apply for Entry-Level Roles & Internships

Look for job titles like Data Engineer, ETL Engineer, and Big Data Engineer. Securing an internship would be great. Focus on companies where you can learn. Use your portfolio to showcase that you understand data engineering fundamentals.

Step 9: Prepare for Data Engineering Interviews

Interview preparation is a must:

• Practice SQL queries, data modelling questions.
• Understand system design for data systems, how to design a pipeline that handles millions of events, and how to ensure reliability and fault-tolerance.
• Know cloud infrastructure, big data platforms, streaming vs batch, and orchestration.
• Be ready for case studies (“design a Twitter-style feed processing system”, “build a data warehouse for e-commerce analytics”, etc.).

If you cover these topics thoroughly, you’ll be well-positioned to secure a data engineer role.

Skills Required to Become a Data Engineer

There are various skills required to become a good data engineer, which is why the role seems a bit complex and challenging, but if you try to learn/practice them one step at a time and then try to mix them up naturally, that is when you’ll truly be able to function without getting lost! Here are some important skills:

Core Skills

• SQL: querying, optimisation, indexing, joins, partitions.
• Python / Scala / Java: scripting, data transformation, building data pipelines.
• Data Modelling: designing schemas, star/snowflake models, dimension/fact tables.
• ETL/ELT: building extract-transform-load workflows, understanding source systems, destination systems.

Advanced Skills

• Cloud computing: AWS, GCP, Azure - storage, compute, managed services.
• Big data frameworks: Hadoop, Spark, Flink - for processing large datasets.
• Streaming & real-time processing: Kafka, Kinesis, Spark Streaming.
• Orchestration tools: Airflow, Prefect, Luigi - building workflows, monitoring, scheduling.
• Data engineering architecture: data lakes, data mesh, data warehouse strategies, scalability, fault-tolerance. Soft Skills
• Problem-solving: real-world data issues are rarely clean; you need to find solutions.
• Communication: You often work with analysts, scientists, and business stakeholders - be able to present technical details in business terms.
• Collaboration: You’ll work cross-functionally across teams, so being a team player is key.
• Attention to detail: Data quality, correctness, and reliability are critical.

Tools & Technologies Every Data Engineer Should Know

Knowing which technologies to use, and how they work together, is what makes data engineering truly effective. Whether it’s learning SQL databases, cloud platforms like AWS or GCP, or workflow orchestration tools like Apache Airflow, being fluent in the right technologies is key to building reliable, scalable, and high-performing data systems.

There are some of the tools listed below that you’ll come across as a data engineer:

• Databases: MySQL, PostgreSQL (relational), MongoDB, Cassandra (NoSQL)
• Data frameworks: Hadoop, Spark, Flink for big data processing
• Cloud services: AWS Redshift, Google BigQuery, Azure Synapse
• Workflow orchestration: Apache Airflow, Prefect, Luigi
• Version control & DevOps: Git, Docker, Kubernetes (in some organisations)

Getting hands-on with these tools gives you confidence and credibility.

Trends Shaping the Future of Data Engineering

Data engineering is changing fast, forced by the explosion of real-time analytics, AI integration, and cloud-native technologies. What used to be about managing static databases has now expanded into automating entire data ecosystems and enabling instant insights. Understanding the emerging trends isn’t just useful, it’s essential for anyone looking to stay relevant and future-ready in a field that’s constantly redefining how data powers the world. The key trends to keep in mind are as follows:

• Rise of real-time analytics & streaming: Businesses increasingly expect real-time data insights rather than batch statements.

• AI-powered data engineering: Automation, machine-learning capabilities built into data pipelines, intelligent data-ops tools.

• DataOps & automation: Data engineering borrowing from DevOps, continuous integration/continuous deployment (CI/CD) for data, monitoring, tests, and governance.

• Shift from Hadoop- cloud-native architectures: Many companies are migrating from on-prem big data clusters to serverless or managed cloud platforms.

• Growing remote/freelance opportunities: With infrastructure in the cloud and many data engineering tasks decoupled from location, remote roles are increasingly common.

These trends mean that as you work through the roadmap, keep your learning fresh and aligned with emerging practices.