Have you ever wondered how companies manage to turn vast oceans of raw data into actionable insights? Data engineering is the hidden engine that powers this transformation. From enabling real-time analytics to supporting groundbreaking machine learning models, data engineering shapes the digital landscape.

This field ensures that businesses can rely on clean, accessible, and secure data to thrive in today’s competitive world. Let’s explore how this fascinating domain forms the backbone of modern technology.

What is Data Engineering?

• Managing large datasets: It facilitates the processing of massive amounts of information quickly and efficiently.
• Integrating data: Combines data from various sources such as databases, APIs, or files.
• Preparing data for analysis: Cleans and transforms data into a suitable format for analytical processes.
• Automating processes: Develops pipelines – automated data processing workflows that minimize human error and speed up data processing.
• Ensuring data security: Protects data from loss and unauthorized access while complying with legal regulations.

The Role of a Data Engineer in an IT Team

A data engineer is a crucial member of teams managing information. They collaborate with various departments, including data scientists, business analysts, and developers. Their main responsibilities include:

• Designing and building data infrastructure: Creating data warehouses, databases, and processing systems.
• Implementing data pipelines: Developing ETL (Extract, Transform, Load) or ELT processes for efficient data processing.
• Monitoring data quality: Deploying tools for validation and cleaning of data.
• Ensuring scalability: Adapting systems to handle growing amounts of data.
• Supporting analytical teams: Providing consistent and ready-to-use data in real-time.
  

Skills Required for a Data Engineer

A Data Engineer’s job demands a wide range of skills, including:

• Database expertise: Knowledge of both relational (SQL) and non-relational (NoSQL, e.g., MongoDB) databases.
• Programming skills: Proficiency in languages like Python, Scala, Java, or R.
• Data processing tools: Familiarity with Hadoop, Apache Spark, Kafka.
• Analytical abilities: Understanding data modeling and statistics.
• Cloud knowledge: Experience with AWS, Google Cloud, or Azure for data processing support.
  

Why is Data Engineering crucial nowedays?

Modern businesses base their decisions on data analysis. Without data engineering, this would be impossible as data would remain scattered, inconsistent, or inaccessible. Thanks to the work of data engineers, organizations can:

• Make fact-based decisions instead of relying on intuition.
• Optimize business processes.
• Develop advanced predictive models.
• Ensure compliance with data protection regulations.

Data engineering lies at the heart of modern information systems and is key to success in the digital economy.

Why did I switch to Data Engineering?

In fact I pivoted from clean Software Engineering to Data Engineering mixed with SE. I will share more insight why I did make it 2 years ago.

After 7 years of working as a Junior, then Mid, and finally Senior Software Engineer, I suddenly found myself at a crossroads. My entire team was dissolved — we had been building an e-commerce logistics tool — and I had to choose between two options:

a) Leave the company just a few months after joining (right after I had finally ramped up) and start looking for another job in a similar role.

b) Stay and help build a brand-new team whose goal would be to create a data platform for most departments in the company — a platform that would support their day-to-day decision-making.

Since I’ve been working with technology for a long time, I knew one thing: tools like Databricks, Snowflake, DBT, or PySpark are just tools. Learning them is basically a normal day at the office for someone with my background. What I didn’t know was whether working with data would actually be interesting.

A bit uncertain, I chose option b. I figured I’d give myself a chance to explore new technologies — maybe it would help me grow. Even though I had only heard about those tools or barely touched them before, I decided to learn them on the job. No courses, no trainings — I was thrown straight into the deep end, and honestly, I loved it. I enjoy working under pressure; that’s when I feel the most productive.

After around three months, I knew I wanted to keep going in this direction. And more tools showed up on my path — PowerBI, MCP (Model Context Protocol), and others. Working with data turned out to be a perfect match for me. I love charts, correlations, tables, forecasting, analysis, and telling stories with data. My role expanded from Data Engineering into the entire spectrum — from Software Engineering to BI Specialist.

I built dozens of dashboards that are still used in the company today and bring real value. Presentation skills could’ve been better — that’s something I know I need to improve. In the tech world you can build incredible things, but if you can’t present them well, your work might never get the attention it deserves.

My coding background also came in handy. Data often had to be collected from somewhere, and quick integrations with external APIs allowed us to expand our data sources at an impressive pace.

This is my story in a nutshell. I don’t want to bore you with all the details, but I do want to share a message: career pivots — especially in IT — will be happening all the time now. Technology has accelerated dramatically. Coding alone is no longer a unique skill. Honestly, even my uncle from a family party could probably build a mobile app now using LLMs (okay, maybe I’m exaggerating a bit).

Developers who never made the jump toward Solution or Software Architecture are facing tough times. Looking back, I think my decision to pivot was risky, but absolutely worth it. Two years ago we didn’t know how much LLMs would change the job market. Today we do — and pure programming isn’t enough anymore. Data engineering, on the other hand, does minimize some of that risk.

In the future, I plan to grow further in Project Management (I already have some experience) and in Solution Architecture. My goal is to become a one-man army — someone who can lead a project with a few AI agents or junior developers. I believe this kind of profile will be the most valuable over the next decade. In short:

• Programming
• Testing
• Data engineering & analysis
• IT Project Management
• Solution & Software Architecture (mostly cloud-based)

If you look at my CV, it’s heading exactly in that direction. And even though I’m once again on the job market, I’m not worried. I know where I’m going, and I know where I came from. And I got here only thanks to hard work and persistence.

The post is getting a bit too personal, so I’ll wrap it up here.

My message to the world? Don’t be afraid to take risks. Sometimes you need to take a step back to move forward — and that step might be exactly what you needed.

Good luck!