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Every dashboard, AI model and board report starts in the same place: with data that someone had to collect, clean and deliver on time. That someone is the data engineer. It is one of the most sought-after roles in Polish IT, and one of the least understood outside the industry.
This guide explains what a data engineer is, what the job looks like day to day, which tools it uses, how it differs from a data scientist, how much data engineers earn in Poland and how to get into the field. If you are looking for a role now, see our current jobs. ITDS also builds nearshore teams from Poland and Portugal for companies across Europe and the US, and data engineering projects make up a large part of that work.
| In this guide | |
| What is a data engineer What they do A typical project Tools Data engineer vs data scientist |
Salaries How to become a data engineer Specialisations Recruitment Data engineering at ITDS FAQ |
What is a data engineer?
A data engineer designs, builds and maintains the systems that move data from where it is created to where someone can use it. Data from applications, transaction systems, files and external APIs reaches a data warehouse or data lake in a form that analysts, data scientists and management can rely on.
You could compare the role to a plumber: nobody thinks about it while everything works. Everyone notices when the morning report is missing or the numbers in two systems don't match.
What does a data engineer do day to day?
The daily work combines programming, architecture and quality control. Typical tasks include:
- Building data pipelines. ETL and ELT processes that extract, transform and load data, in batches or in real time.
- Designing warehouses and data models. Table structures that make analysis easy and stay fast at large volumes.
- Looking after data quality. Tests, monitoring and alerts that catch errors before they reach the reports.
- Optimisation. Speeding up queries, cutting cloud costs and tidying up data flows.
- Collaboration. With the analysts and data scientists who use the data, and with the IT teams who produce it.
On projects ITDS recruits for, data engineers build consolidated batch and streaming pipelines, data warehouses and cloud migrations, often for financial services clients.
A typical project: from source to report
Imagine a bank that wants to know the credit risk of its whole portfolio every morning.
First, the data engineer works out where the data comes from: the transaction system, the lending system and an external credit bureau. Then they build a pipeline that pulls new records every night, checks they are complete, standardises the formats and joins everything in one warehouse.
Next come the quality tests. If the number of records suddenly halves or empty fields appear, the team gets an alert before management sees the wrong numbers. Finally, the engineer prepares the tables that analysts use for the report and data scientists use for the risk model.
When everything works, nobody thinks about it. The report is simply there in the morning. That is the measure of a data engineer's work done well.
Tools and technologies for working with data
The exact set of technologies depends on the company, but a few groups of tools appear in most job ads.
| Area | Typical tools |
|---|---|
| Languages | SQL, Python, sometimes Scala or Java |
| Data processing | Apache Spark, Kafka, dbt, Airflow |
| Cloud | Azure, AWS, Google Cloud and their data services |
| Warehouses and platforms | Snowflake, BigQuery, Databricks, Synapse |
| Infrastructure | Docker, Kubernetes, CI/CD, infrastructure as code |
You don't need to know all of them. What matters more is excellent SQL and Python, and an understanding of how to design a data flow that doesn't break. Tools change every few years; the principles stay.
Data engineer vs data scientist: the difference
Both roles deal with data and work closely together, but they are responsible for different things.
| Data engineer | Data scientist | |
|---|---|---|
| Main task | Deliver data quickly, reliably and in good quality | Draw conclusions from data and build predictive models |
| Output | Pipelines, warehouses, data platforms | Models, analyses, recommendations |
| Closest to | Software engineering | Statistics and the business |
| Key skills | SQL, Python, data architecture, cloud | Python, statistics, machine learning |
In practice, a data scientist without a data engineer spends most of their time finding and cleaning data. That is why companies that take AI seriously hire data engineers first. We cover the other side of this partnership in our guide to the data scientist role.
| Without a data engineer there is no data. Without data there is no AI, no reports and no decisions. |
How much does a data engineer earn in Poland?
Data engineering is one of the well-paid specialisations in Polish IT, and demand for experienced data engineers remains high. Pay depends on experience, cloud skills, industry and contract type.
| Level | Experience | Employment contract, gross per month (approximate) |
|---|---|---|
| Junior | 0–2 years | about PLN 8,000–12,000 |
| Mid | 2–5 years | about PLN 14,000–21,000 |
| Senior | 5+ years | about PLN 21,000–30,000 |
These ranges are approximate and based on public summaries of 2026 job offers. Compare current offers before you decide, because figures change quickly.
At mid and senior level, most data engineers work on a B2B contract, which usually pays more net at higher rates. When comparing offers, remember the costs you cover yourself on B2B: social security (ZUS), holidays, sick leave and gaps between projects. The highest rates go to people with experience in the cloud, streaming and large data platforms.
| Looking for a data engineering project? We recruit data engineers for long-term projects with international clients. See jobs |
How to become a data engineer
There are several routes into data engineering. Backend developers, data analysts, database administrators and computer science graduates are the most common. A few steps work whatever your starting point:
- Master SQL. It is the most important tool in the job and the most common topic in recruitment tasks.
- Learn Python for data processing. Focus on working with files, APIs and data libraries.
- Build an end-to-end pipeline. Pull data from a public API, transform it, load it into a database and run the whole process automatically on a schedule. Publish the code.
- Learn one cloud. An entry-level certificate in Azure, AWS or Google Cloud helps you through the first screening.
- Understand data modelling. Warehouses, fact and dimension tables, and the difference between batch and streaming.
Specialisations: where a data engineer can grow
Data engineering is a broad field, and after a few years most people specialise. The most common directions are:
- Analytics engineer. Between engineering and analytics. Models data in the warehouse so analysts can use it without help from IT.
- Platform engineer. Builds and maintains the company's whole data platform, including infrastructure, security and access.
- Streaming engineer. Specialises in real-time data, such as transactions or application events.
- ML and MLOps engineer. Feeds data to AI models and automates their training and deployment.
- Data architect. Designs solutions for the whole organisation and makes sure they fit together.
In financial services, where ITDS runs many projects, there is one more dimension: regulation. A data engineer who understands data protection, audit and reporting requirements is especially valuable.
Recruitment: what to expect
Most processes include a recruiter call, an SQL test, a practical task and a technical interview. The technical interview often includes system design: how you would build a pipeline for a given data volume, handle errors and ensure quality.
Recruiters look for people who think about reliability and cost, not only about making the code run once. A concrete example from your own work, such as a fixed outage or a faster process, often weighs more than a list of tools on your CV.
Projects and collaboration at ITDS
ITDS is an IT staffing and nearshore company with teams in Poland and Portugal, part of House of HR. We recruit data engineers for projects with international clients, mainly in financial services, working on data warehouses, cloud migrations and data platforms.
The process is short: application, recruiter call, technical interview with a specialist, meeting the client team and a clear offer with the contract type and rate. Our projects usually run for a long time, and we stay in touch once you start.
Data engineer: frequently asked questions
A data engineer designs, builds and maintains the systems that collect, process and deliver data. They create the data pipelines, warehouses and platforms that analysts, data scientists and the business rely on.
As a rough guide, a junior earns about PLN 8,000–12,000 gross a month on an employment contract, a mid about PLN 14,000–21,000 and a senior about PLN 21,000–30,000. Many mid and senior engineers work on B2B. Pay depends on experience, cloud skills and industry.
A data engineer delivers data and builds the infrastructure, while a data scientist analyses the data and builds predictive models. The data engineer is closer to software engineering, the data scientist to statistics and the business.
Above all, excellent SQL and Python, knowledge of at least one cloud, data processing tools such as Spark or Airflow, and the basics of data modelling and warehousing.
Yes. Many data engineering projects are remote or hybrid, because the work happens in the cloud and on platforms you can reach from anywhere. The details depend on the client and the project.
Find your next data engineering projectWe recruit data engineers for long-term projects with international clients, with a clear contract from day one. See current offers and apply in a few minutes. Companies looking for specialists can learn about our nearshore teams. |